Marmeto is one of India&#x27;s leading Shopify and Shopify Plus solution providers for all ecommerce stores with customized solutions. We have developed over 900+ Shopify websites, 60+ Shopify Plus stores and have successful collaborations with an array of partner ranging from marketing to logistics ...

Marmeto | Enabling Commerce

Marmeto is an Information Technology company. We have one aspiration – to build commerce better for everyone. We have one belief – hard work with 100% dedication always yields complete client satisfaction, which is what we are constantly working towards. Other than that, it’s ... Read more

Working at Marmeto | Glassdoor

&quot;Marmeto is a startup that helps clients with their Shopify customisations. Their service offerings include OTP solutions, auto address update, multi-currency checkout, checkout UI revamp, two-step checkout, prepaid discount script, mobile validation, pincode validation, additional COD charges, ...

Marmeto Company Profile, information, investors, valuation & Funding

Marmeto | Enabling Commerce | 11,104 followers on LinkedIn. India&#x27;s Best E-commerce Partners #enablingecommerce #thebestofecommerce | We’re a proud Indian e-commerce focused Company. We’re e-commerce growth partners specializing in e-commerce design and development services that helps brands ...

Marmeto | Enabling Commerce | LinkedIn

Marmeto is an IT solution company that develops Shopify e-commerce platforms.

Marmeto - Crunchbase Company Profile & Funding

Marmeto is an Information Technology company. We have one aspiration – to become a top-notch business in the service industry. And we have one belief – hard work with 100% dedication always yields complete client satisfaction, which is what we are constantly working towards.

9 Marmeto Jobs in India | Marmeto careers | Cutshort

Established around mid 2017, Marmeto registered exceptional success in a very short span of time. The company was able to scale exponentially due to the diverse minds who sowed this idea and then worked from scratch to place Marmeto at its current heights. Shashwat Swaroop, an engineering drop ...

Marmeto: Bootstrapped And Growing At Full Tilt

Marmeto - email id &amp; phone of 1 top management contacts like Founder, CEO, CFO, CMO, CTO, Marketing or HR or Finance head &amp; all company details.

Marmeto - Email & Phone of top management contacts

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Marmeto - Business Support Services Startup, Delhi

71 Marmeto reviews. A free inside look at company reviews and salaries posted anonymously by employees.

Marmeto Reviews | Glassdoor

Marmeto is an Information Technology company. Our aspiration â€“ to become a top-notch business in the service industry and to develop awesome tech products that help businesses scale to unimaginable heights. We have one belief â€“ hard work with 100% dedication always yields complete ...

Marmeto: Revenue, Competitors, Alternatives

Marmeto | Shopify Plus Accelerator developed an e-commerce website for a fashion company. The team focused on redesigning the product page, especially the photos of the products in order to increase conversion.

Marmeto | Shopify Plus Accelerator Client Reviews | Clutch.co

There is no pros so far · 1. Directionless Company 2. No working process 3. No motivation of work 4. Make your work like a slave (make you sit in an office 12-14 hrs a day, even if your work is done) 5. Will hire you, make their work done and terminate you by giving a lame excuse.

Marmeto - One of the Worst Company | Glassdoor

Excellent workplace and well balanced management for professional career and personal life .I am working in best environment work culture. Happy to be part of Marmeto family. ... Smooth onboarding process, great company culture, flexible working times, and a friendly environment to work in. ...

Working at Marmeto: Employee Reviews | Indeed.com

Find out what works well at Marmeto from the people who know best. Get the inside scoop on jobs, salaries, top office locations, and CEO insights. Compare pay for popular roles and read about the team’s work-life balance. Uncover why Marmeto is the best company for you.

Marmeto Careers and Employment | Indeed.com

This employer has claimed their ... your company compare?Get started with your Free Employer Profile to respond to reviews, see who is viewing your profile, and share your brand story with top talent. ... To filter interviews, Sign In or Register. ... To filter interviews, Sign In or Register. ... I applied through other source. The process took 1 week. I interviewed at Marmeto (Bengaluru) ...

90 Marmeto Interview Questions & Answers (2023) | Glassdoor

Marmeto is a Bangalore, India headquartered Company with presence across US, UK and Germany

Marmeto

Originally Published on Inbound.org as an Inbound Original. A couple of months ago the marketing team at started kicking around the idea of switching from HubSpot to another more robust system. As we grew our marketing and demand generation campaigns, we needed a more customizable tool that had a deeper integration with Salesforce and would scale well, both operationally and financially. Uberflip HubSpot was a great tool to start building our processes for campaigns with, but as we got more complex, we hit certain limitations. The lack of customization options and no Salesforce integration started to hinder our efforts. For example, we couldn’t create our own Lifecycle stages to reflect the realities of our marketing funnel. When we started the quest to migrate to a different platform, it came down to Marketo and Pardot. The platforms are very similar, each with their own specific quirks and areas where they excel. Ultimately, the winner was Marketo (despite the Pardot sales team making many house calls at the office). Migrating systems of any kind is a monster project. I’ll share some experiences and process below in case anyone going through their own migration finds it helpful. For context, our team is four people, we have a current contact DB size of 90,000+, and a large number of creative and operational items. Please note: This is the migration from our internal marketing perspective and there were no changes to our Uberflip Platform integrations with major Marketing Automation platform_s._ The Migration Process Typical MA (Marketing Automation) migrations take at least a month or two months if not more. We had just under a month to flip the switch (yes, the Uberflip has a Podcast of the same name — we like to stay on brand). We recruited (a Marketo agency) to help us manage the project and minimize team disruption, since our team still had our regular demand generation programs to run. With a project of this scale, we had to ensure the business and operations kept running while we slowly switched things over on the back end Perkuto We kicked off the migration with Perkuto by having (their Solutions Architect) spend a day with us. We walked him through the operational workflows we had in HubSpot, scoring models, landing page templates, email templates, hand-off processes, data schema, sales processes, and general business roles in Salesforce and HubSpot. Justin Norris Choosing an MVP Migration Route Usually migrations of this scale take a few months to complete, yet our HubSpot license was expiring so our timeframe was truncated. To help make it so we didn’t have to work on the migration 24 hours a day, we decided on an MVP migration scope. MVP in this scenario stands for minimum viable project. We had to figure out what would be the bare minimum for us to migrate over so that things would still function normally and we could hit our deadline. We ended up migrating our database, campaign templates, and operational workflows over first and then later built it to be more robust after the basic elements were done. If we had more time, we would build out our full funnel nurtures, more templates, in-depth scoring models, tested the Bizible/Marketo integration, and operational workflows for campaigns. Tip: Doing the work up front to inventory everything, as painful as it is, will make for smooth sailing but don’t spend too much time buried in documentation. A great upside to a migration is that you can get rid of all the legacy ‘junk’ that collects over time and start fresh. The downside is that the amount of moving parts can be overwhelming. Based on our discussions, Justin put together a Technical Design Document (TDD) that served as the bible for the migration. The TDD acted as more of a formal project management document, so for keeping track, taking inventory, and marking off to-dos, we used a Migration Workbook (shown below). Tip: Involve your Demand Generation team or anyone else who spends their day in your Marketing Automation system, but don’t have too many cooks in the kitchen. Otherwise, there will be a ‘paralysis-by-analysis’ type situation. In our case, we included our four-person demand team, and between each of us, we broke down what we were going to do and trusted each other to get the job done. Syncing Salesforce Our system of record is Salesforce, so before tackling any of the operational and campaign items, we needed to connect Salesforce with Marketo for our leads to start syncing. (Marketing Ops) and (Inbound Marketing) segmented our 90,000+ contact database into ‘Marketo Sync’ and ‘Discard’. The discard pile was made up of free email addresses (Gmail, Yahoo, etc.), hard-bounced emails, and anyone who has had zero activity within the past year. Sibil Tara These ‘junk’ contacts still existed as a backup in our Salesforce instance, but they would not be migrated to Marketo. To segment the database, we created a custom checkbox field labeled ‘sync to Marketo’ and using a Smart List to filter, any contacts that were to be migrated were marked as ‘true’ using a HubSpot Workflow. This checkbox was then synced with Salesforce for all ‘true’ contacts. Since our Salesforce has our entire marketing/sales database, we had a backup of all contacts ever created there. But to keep our shiny new Marketo instance clean, we only synced the contacts that were actually good. Quirk: There were some issues with setting up the filter in Marketo. It is not a common practice to do this, but it is possible. We had to call Marketo support for this multiple times. Tip: We migrated over all the ‘opt-outs’ to Marketo due to CAN-SPAM regulations of having an up-to-date list on our current platform. For those who already had opted in, we did not re-opt-in from Marketo. Data Schema In HubSpot, we had years of custom fields, one-off fields, checkboxes, and other assorted items. Important note here — these fields were only in HubSpot and not in Salesforce, so there was no Salesforce backup like we had for other items. Using the HubSpot-Salesforce integration and field mapping, we first made a list of all the correct mappings between the two systems so we could replicate it. We then sat down as a team and looked at all the Marketing-only fields and debated if they were worth keeping or not. Some were marked as ‘non MVP’ meaning we would revisit them later. Others that were essential for campaign reporting were recreated in Marketo using the same data types to ensure compatibility. Tip: Ensure you’re mapping the field types correctly. Otherwise, the dates will end up as numbers and numbers as text. Especially for dates, having it as a ‘date’ type makes it easier to segment your contacts using date selection tools. Templates Forms and Templates & CSS: Tara took lead on landing pages and emails to determine which ones were actually being used and which ones were ‘tests’ or one-offs. This meant going through each asset to determine if it was in use or going to be used and what exactly it was being used for. We provided the templates within HubSpot plus screenshots and expected editable areas to Perkuto who then recreated those into Marketo. In some cases, you might be able to use the same HTML/CSS between HubSpot and Marketo as long as you remove the system scripting language. We sometimes had to jump in and do some fine tuning and QA, especially for the forms. Case in point: in Marketo, if you set the form width using the form editor, it will add in-line styling for each field that will overwrite any custom CSS you add. Best practice for us is to add styling to the parent class in the CSS instead to keep it consistent and easier to edit (as with the image above — don’t add inline styles). With landing pages, they are non-responsive by default so we had to go in and add an ‘HTML Box’ to the template to include some media queries which was surprising. Quirks: In Marketo, there is no universal header or footer code section for landing pages. Rather, it is done through a universal token. In HubSpot, it’s relatively easy to add scripts across all HubSpot pages by including them within the universal <head> or </body> section. It seems as though Marketo does not allow for this. If you edit any asset, it will be saved as a draft until you approve it. To see the changes you will need to approve it first. If the asset (like a form that is being used by a landing page) is edited, the landing page will automatically be saved as a draft. So make sure you save the form, approve it, and approve the landing page as well or none of the changes will be live. Marketo form classes are tricky but using something like Firebug will help grab the right selector (but it can make you want to pull your hair out). Pay close attention to checkboxes on forms and their alignment If your value from the dropdown in a form isn’t displaying but still passing into Marketo as a data value, check the padding. Marketo doesn’t support dropdown fields for custom values. As a workaround, you can create the field in Salesforce and then push it to Marketo. Workflows & Scoring Model Our operational workflows control the lead management in our Marketing Automation system. The most red-zone item for us was our assignment rules and demo request assignments. Perkuto took lead on these, setting them up and we QA’d five times to make sure leads were flowing through. Quirks: There’s a small menu to select and/or conditions for triggers. If you’re triggering rules, double check if they’re based on ‘data value change’ or ‘blank’. This caused us some headaches. Lists of all sorts Lists. Love them or hate them but they’re the heart of segmentation and automation. We cooped ourselves in a room with some music and started going through Smart and Static Lists to determine which ones we wanted to keep and which ones were dispensable. For the Static Lists, in some cases it made sense to combine multiple lists into one ‘master list’ for easier list management. In that case, we created a new Smart List that triggered static list memberships and exported those as CSVs for backup and importing back to Marketo. For the Smart Lists, it was a little more complex. Most of our Smart Lists rely on fields that only existed in HubSpot and not Salesforce. We wanted to keep our SF schema clean. With those fields absent in Marketo, it was impossible to recreate those Smart Lists. To work around this, we created the custom fields as needed per the Smart Lists we wanted to recreate in Marketo. It was a tedious task, but it was essential. After creating the fields in Marketo and creating the right folder structure, we would export the list from HubSpot with emails plus the custom fields selected and populate those fields back in Marketo. In some cases, we combined multiple Smart Lists into a single Smart List and imported those into Marketo. Quirks: Marketo will create a lead without an email. Be wary of that. When importing a CSV file, select CSV from the file type in the import menu and don’t rely on the ‘Auto Detect’ default option. Hub & CTAs and Placements Another additional step in our migration process was integrating our Uberflip Hub with Marketo. For the Hub CTAs, we got help from our wonderful content team to replicate each CTA and make a static list in Marketo that it would feed into. After some QA and making sure all the data fields were populating with the correct parameters, we set it aside for a day before launch. Quirks: We had to make sure we did not add any placements until the ‘go-live’ day otherwise we would have a mashup of HubSpot and Marketo CTAs in the hub. Make sure the right parameters in the CTA are created as a hidden field. Naming Convention Marketo is folder based, which means naming convention and structure is very important. We spent some time structuring our marketing campaigns and activities so everything was set up correctly Having a proper naming convention is a good idea in general, but with Marketo it is crucial to have a consistent naming convention so things are easier to find and work with. In our case, we set it up as the following: Active Marketing Programs: 2016 Marketing Webinars 2016 — DD — Campaign Name Content Syndication CS — 2016 — Asset Name — Channel Name Social Events 2016 — Event Name Evergreen Programs Nurtures 2016 — TOFU — Nurture General Naming Convention: YY- DD — Asset/Campaign Name — Channel Name Quality Assurance and Testing Once everything was uploaded into Marketo, we tested the templates, forms, and workflows for ‘red’ items and set up some ‘Red Zone’ campaigns (like our ABM Hacks series in Marketo). There were a few bugs we came across on the forms, like the drop-down field having extra padding which meant a user’s selection would not display even though it would get passed into Marketo. Some workflows were set to trigger on data value change, but it did not trigger based on a known value off a blank value. Another unexpected issue was the DKIM value. Marketo uses ‘m1_key’ which means that we don’t have Marketo authorized to send emails on our behalf. Our quality assurance was done ad-hoc due to the fact that we had already turned off HubSpot and we were live on Marketo. The team worked hard and fast to make sure nothing important was broken. Lessons Learned In retrospect, we should have communicated the switch-over to our subscribers. It seems like something that wouldn’t make a difference to them since it is all done in the back end, but if something went wrong, they would know why. In one case, an email that went out from HubSpot before the switchover had dead links and missing images if it was opened it after the migration (a gap of three days). In total, our MVP migration took three weeks to complete. We have a slight learning curve with Marketo after using HubSpot as we’re figuring out its different setups and quirks. Overall, the migration went smoothly. Some things to keep in mind: If you decide to migrate, make sure you have everything documented before you begin. In general to keep a healthy Marketing Automation system, have a clean-up day every six months. This will keep the house in order. Don’t sweat the details. Most Marketing Automation systems do what you need them to do really, really well. Just make sure you get a walk through of the important features. For us, it was the ability to really customize our campaigns and nurtures and the ability for sales to enroll a contact into a Marketo campaign directly from their Salesforce account. Test everything. Especially web forms, email delivery, and responsiveness of email and landing page templates. I cannot stress the testing enough. Test all operational workflows, data syncing to Salesforce, and campaign setups. In our case, we had to test Bizible integrations as well. Huge thanks to , , , , and from Uberflip as well as , , and from Perkuto for pulling this off. Three weeks is an insanely short amount of time to off a migration. I still remember Justin saying, “Typically a migration takes months, three weeks is unheard of!” But we pulled it off. Massive kudos to everyone involved! Tara Sibil Shannon Victoria Kelly Justin Cat Eric Adam Questions? Come meet me at The Uberflip Experience!

Republished: Migrating from Hubspot to Marketo in 3 Weeks.

The chemistry of converting raw data into golden opportunities for demand generation is a mastery sought by many but perfected by few. The intersection of data and analytics with demand generation represents not merely a trend but a fundamental shift in how businesses approach market engagement and customer acquisition. With precision analytics, companies are not just passively observing market behaviors; they are actively predicting and shaping future demands. Recently, I interviewed , a Demand Gen Lead at . Steve Armenti Google Can you share a bit about yourself? I’m from New Hampshire and lived most of my life in New England. After college, I landed a sales job at an accounting software company outside of Boston. I was an SDR making 100 phone calls a day trying to book meetings with CFOs. This was 10 years ago before marketing and sales automation was as prevalent as it is today. However, I taught myself how to manually segment my territory using spreadsheets and designed my own A/B email experiments to get meetings. Eventually, I stopped making phone calls altogether and was producing the highest numbers on the team purely from emails. I didn’t know it but I was using ABM + personalization. Being in sales taught me about the human element of marketing and sales. At the end of every marketing campaign is an actual person who is trying to solve problems. In marketing, we often forget that as we sit in our office buildings (or home offices) designing campaigns. I try to remember that there are real people on the other end, just like us, and they can smell your bullsh*t from a mile away. How does data-driven decision-making impact B2B demand generation? In my opinion, it’s everything. I’ve built most of my career on using data to inform a variety of decisions: small and large about B2B demand generation and growth. Data can remove human error and opinion from the situation. For example, let’s say marketing and sales are feuding about the quality of MQLs. MQL quality can be very subjective. What matters is alignment between marketing and sales on what accounts you’re targeting, who in those accounts you’re acquiring, and what are their pain points. Who is the ideal customer profile? All of those things can be measured with data. You can pull data to show that an MQL came from a target account and that the persona is precisely the proper seniority, title, and job function. The data is used to establish a baseline of what is true and what is opinion. If you don’t have the data to prove what is true, go get it before you get into a shouting match. What specific data points are crucial for understanding B2B buyer behavior? I like to first look at a combination of demographics, firmographics, and psychographics. You can get these data points from 1P and 3P research, persona development, your website and marketing data, your CRM, surveys, and focus groups. This will give you a basic understanding of who is in your total addressable market. Second, I want to look at my data. How do customers behave? This data requires analyzing the person-level data against all of your marketing activities. The question you want to answer is “How does [persona] engage with [marketing campaign] and what is the behavior they are taking as a result” my Doing this exercise at scale will unlock a trove of insights about your customer that you can then organize and segment to fit the needs of your campaigns. What role does predictive analytics play in B2B demand generation? Predictive analytics can play a big role in any B2B demand gen program. It requires a solid data analytics infrastructure though first. Assuming there is a data warehouse and process in place to clean and ingest data, predictive analytics can help marketers do many things: Identify the highest propensity accounts, personas, and segments. Predict the timing of purchase intent helping your sellers reach out at the most opportune time Personalize messages or notifications based on user behavior Predict the effectiveness of a campaign or activity And now, with AI, this entire process is speeding up. AI will become better at collecting and processing large amounts of data to identify patterns and trends that marketers can take action on in real-time. As the prediction engine runs, and AI is feeding results back even faster than ever, marketers shift their role to the entire operation versus managing the hands-on creation process of marketing campaigns. directing How can businesses effectively leverage CRM data in demand-generation efforts? CRM data is a goldmine of information. It’s the source code to what makes your customers tick and what incentivizes them to buy. CRM data can be used to identify which accounts and personas have the highest propensity to buy. CRM data can be used to segment your audience into different groups based on their needs, interests, and purchase behavior. CRM data can be used to optimize your campaigns and improve performance. At the end of the day, all marketing should be measured on its ability to drive revenue – and your CRM is one of the most critical data sources to be able to do that. What are the common challenges in collecting and utilizing data for demand generation? Data silos. When data is stored in different systems and applications, it becomes really difficult to access and analyze. The accuracy and completeness of your data is important once you break down the silos. As they say, garbage in garbage out. So if you organized the data but it’s still crap quality, you aren’t even close. Collecting and utilizing data for demand generation can be a resource-intensive process often requiring skilled data scientists. This is a challenge in itself. Proper data infrastructure is essential to good B2B demand generation. That said, I think a big challenge the industry is facing is a lack of analytical skills for the average marketer. The marketer of the future needs to be as analytical as they are creative. How do you ensure data quality and accuracy in B2B demand generation campaigns? It starts with cleaning the data. Make sure you have a process to clean and join together the various data sources you want to use. You might find you are missing certain quality markers so you will need to look at enriching the data through other 1P sources or by licensing 3P data. Once you have a complete picture of your data (often called a data dictionary), you will want to validate that the data is accurate either through testing and QA or with an actual validation tool. Could you explain how data segmentation enhances B2B lead nurturing? Data segmentation is all about dividing your leads and contacts into groups based on certain characteristics, such as industry, company size, job title, location, or historical behavior. You want to use segmentation so you can tailor your marketing and sales messages to each group, increasing the chances they will take the action you want them to take. Using segmentation allows you to improve your targeting and make it more precise, increase the relevance and personalization of your email, and improve the efficiency of email as a channel overall. You send less messages that produce better results. What tools and technologies are indispensable for data-driven B2B demand generation? There are a million of them but you will want a demand platform, marketing automation platform, CRM, customer data platform, web/SEO/content analytics, social media software, and A/B testing software. I’m partial to , , , , , and . , , , , and are also great products. Demandbase Marketo Salesforce GrowthLoop Google Analytics SproutSocial HubSpot Kissmetrics Mailchimp Optimizely Buffer How does personalization based on data improve B2B lead conversion rates? At a high level, when you personalize your message, you show people that you understand their needs and interests. This can make them more likely to pay attention to your messages and grow an affinity toward your brand. This will likely increase the chances that people will engage with your marketing and do what you want them to do. Personalization is a game of compounding value. As your audience continues to feel like you understand them and can meet their needs, they go deeper into the funnel evaluating your product or service. Marketing continues to personalize until the person raises their hand and takes action or wants to talk to sales. The personalization doesn’t stop here. Onboarding, sales enablement, and product discovery all need to continue the experience the person felt during the evaluation process. How do you bridge the gap between data analytics and actionable demand-generation strategies? In my opinion, it’s a linear process that goes like this: Get your house (data warehouse) in order. Collect, clean, and ingest all the data you want into one source of truth Build the proper data pipelines to fuel your teams. Data needs to flow from the warehouse into dashboards, CDPs, reporting, etc. so teams can review that data in real-time Automate insights and reporting. Most of the regular, day-to-day reporting you need to do is simply looking at the same KPIs weekly and/or monthly. Standardize those KPIs and the reports. Set calendar invites for when you want your team to look at it. Pull your top insights and develop commentary about what changed from the last reporting period and why. Run optimization sprints. After you’ve collected insights, rank the top ones using some priority method [e.g. Reach, Interact, Convert, and Engage (RICE)] and go and A/B optimizations. Do this in a sprint fashion so you get quick feedback and quick data. That feedback and data flows back into your pipelines and the cycle becomes a perfect loop. What emerging trends in data and analytics are shaping the future of B2B demand generation? AI, of course. I’m stoked about what AI is going to do for B2B demand generation. On the data side, I’m personally exploring how AI can automate the collection and analysis of data, improve insights and decision-making, and identify patterns and trends in data that go unseen by humans to better understand customers and prospects. On the creative side, I’m excited about personalizing our marketing to each person. Right now I use a framework that identifies target accounts, ICP (personas), and then segments. The next level of this is down to the actual person. I’m also really interested in AI for the use of predictive performance and analysis in demand generation. Launching campaigns is a time-consuming process and when it fails, a lot of resources, budget, and time are wasted. With AI, predicting the success rate of campaigns is possible. Additionally, prediction around which accounts, personas, and people are most likely to buy is incredible. This plus the personalization is like Michael Jordan and Scottie Pippen in the 90s – unstoppable. Also published . here

What Google Can Teach Us About Effective B2B Demand Generation

Carpenters who have the right tools and know how to use them effectively will succeed, but having a hammer, nails, saw, and other equipment is not enough to guarantee success. the same thing goes with sales reps. Communication skills, tools, and industry knowledge are nothing unless combined to be used effectively in the right place and time. in this article, we will discuss sales enablement software and how to choose one for B2B companies with insights from: Director at and sales resources/ tools expert , James Donaldson Sales Reader Founder & CEO at David McClean SDR Systems Group Plus eight sales enablement software to consider for B2B companies. What is sales enablement? “Solutions, processes, and tools that help to create a more efficient sales team, aim to save time, track activity, and engage with prospects and potential customers effectively,” said James. Put simply, the main goal of a sales enablement strategy is revenue. Sales reps are on the frontline when it comes to revenue targets. That said, setting up the process that leads to revenue growth is a must for them. The process involves; defining measurable revenue goals, developing training, tracking progress, adapting, learning, and utilizing technology to make better use of data. By doing this you enable your sales reps to do their best and sell. The question is; How to set up this process and drive efficiency from the sales team? According to reps say that the biggest challenge for them is the lack of time and resources to perform their job. Considering the length of B2B transactions, this means that a large amount of sales reps aren’t doing what they have been hired to do, which is selling. Gitnux, 65% of sales That’s why having sales enablement software as a single source of truth that sales reps can leverage to best interact with prospects could be the answer to the “How” question. What are the common pain points of B2B sales reps The common pain points and shared with me were: James David Lack of activity Time-consuming processes personalization and data optimization Lack of alignment between marketing and product team Training and onboarding Those are just a few, but common. Putting the effort to face those pain points manually is a losing step to take. Data from Forrester shows that companies that align people, processes, and technology across their sales and marketing teams, achieve more revenue growth and up to more profitability. 36% 28% Those stats don’t come from thin air. Facing pain points and solving problems are the main drivers for such numbers. The data from Forrester show the real essence of sales enablement and that aligning People, processes, and technology is a winning strategy. Now let’s see: What is actually sales enablement software? Sales enablement software centralizes the use of tools, technologies, and resources designed to empower sales teams and improve their effectiveness throughout the sales cycle. Sales enablement software also helps sales reps access the right information, content, and support at the right time, enabling them to engage with prospects more efficiently and close deals more effectively. The use cases of sales enablement software are wide which also makes choosing the right one a pain point for sales managers. The main goal of this article is to discuss this pain point and to give a piece of advice on how to choose the right one, so before we answer this question, let’s take a look at the types of sales enablement software. What are the types of sales enablement software? The fact that many SaaS products are nearly the same these days, doesn’t mean that all of them have the same features or value. It’s your lucky day if you come across a company that is known to deliver on its promise and has unique messaging, If not, then it’s your job to take a look at the market and understand the types and wide usage of the product. Here are the types of sales enablement software/platform/tools: Organizes and distributes sales-related content. Sales Content Management Software: Provides coaching and training to improve sales skills. Sales Coaching Software: Monitors and optimizes sales team performance. Sales Performance Management Software: Provides insights on potential leads and prospects. Sales Intelligence Software: Offers insights into prospect interests and intentions. Intent Data Providers: Tracks engagement with sales emails. Sales Email Tracking Tools: Forecasts sales trends using data analysis. Predictive Sales Analytics Tools: Aligns sales, marketing, and support operations for revenue optimization. Revenue Operations Platforms: How to choose the right sales enablement software? Now we’ve listed the types of sales enablement platforms you probably wondering how to choose the right one that best integrates with your systems and serves your objectives “You need to understand the other tools already in place and the efficiency of the team. You often could remove certain activities and unnecessary work from the team, to give them more prospecting and sales-focused time. Things will improve if the right data, tracking, and training are in place. If those elements are in place, and the team is truly multi-channel then bringing sales enablement software begins to make sense.” Says James Donaldson. also adds: It would be best to look at your software stack and its limitations. If you have any gaps, you must fix those gaps by adding new software. This can mean that you don’t have a solution in place or your solution is not robust enough. David McClean The right software for you, is the one that fills the gap in your sales process and centralizes data sources, and integrates with your systems. You need to see where things go wrong in your sales cycle, what are the questions that make sales reps remain silent during sales calls, and what makes the prospect churn while they are almost converted. The takeaway: The thing is, a lot of questions could be brought to the table regarding your sales performance. It could be anything look inside and analyze your sales calls. Eight sales enablement software to consider in 2023 1. — Best for product related-documentation and sales content Highspot Enterprises can deliver sales enablement utilizing the Highspot platform, which uses AI-powered search, contextual training, application support, and guided selling. Highspot's Sales Content Management makes sure that any resource or solution sales reps might need is readily available thanks to its sophisticated filtering tools. Based on the details of the lead record, sales representatives have access to content and email templates. Highspot also provides knowledgeable recommendations for product-related materials that sellers may share with prospects, such as product overview decks, case studies, and more. With the help of the Chrome extension from Highspot, sales representatives may also access this information in Gmail when emailing prospects, saving them from having to transfer between their inbox and the location of the documentation. Key features: : lets marketing and sales teams understand how the content is performing across the sales cycle and optimize it accordingly. Advanced analytics : AI-powered recommendations, predictive content, and analytics Content personalization : Sales plays that deliver step-by-step guidance so sales reps can get up-to-speed quickly Content delivery Pricing: No info provided, contact for a custom quote. G2 reviews and rating 2. Best for centralized sales management Seismic — Seismic provides a central location with solutions for global version control and approval where all sales resources, data, and content can be managed. For any buyer engagement, having access to pertinent information and customized content helps to ensure optimal sales efficiency. While sales enablement technologies assist in locating pertinent material, creating such content is a significant portion of the task. Your marketing team may develop engaging material to influence the consumer in your direction with the use of instructional design software and content curation tools. Key features: Seismic curates content like the latest news, competitive intel, product updates, and more to equip your sales team with the knowledge and information they need to engage buyers effectively. The predictive content recommendation feature suggests relevant content during the right stage of the selling process Pricing: No info provided, contact for a custom quote. G2 reviews and rating 3. — best for sales content management Showpad A content management sales enablement platform called Showpad aids sales representatives in finding pertinent content at the appropriate time. Even though marketers think they're producing a lot of content, 65% of sales representatives claim they can't find information to send to prospects when they contact a buyer. Sales reps can receive recommendations for pertinent content using systems like Showpad that are based on machine learning and marketing principles. Key features: Intelligent search and additional filtering allow sellers to quickly find the content they need to drive any deal forward. Showpad integrates with a host of applications like Gmail, Outlook, Salesforce, MS Dynamics, and Marketo, cloud storage like Box, Dropbox, and Drive, and social media platforms like LinkedIn, Facebook, Twitter, and WhatsApp. Pricing: No info provided, contact for a custom quote. G2 reviews and rating 4. : best for coaching and training Mindtickle A sales preparedness tool called Mindtickle assists businesses in raising revenue by enhancing knowledge, comprehending optimum sales habits, and adjusting to change. Use Mindtickle to develop your Ideal Rep ProfileTM, quickly create learning programs, assess how knowledge is applied in sales meetings, and enhance the entire sales process. Mindticle is the number 1# on G2 when it comes to ease of use. Mindtickle is taking the lead day by day in the field of empowering sales reps. Key features: Pricing: No info provided, contact for a custom quote. G2 reviews and rating 5. Best for sales automation Outreach: With an efficient pipeline that streamlines your process, the sales engagement platform Outreach improves communication between your sales teams. With fully functional voice and email and clever automation, this solution aims to increase the number of meetings and increase participation. Sales representatives can learn when to call and respond to prospects using Outreach's Analytics and Reporting tools. Your sales playbook is effectively automated by this system, including follow-ups, scheduling, and outbound and inbound sales. Key features: Sequences that help sales agents determine when and how every customer interaction should happen Always-on assistant that extracts key customer information and auto-updates records AI-powered machine learning that discovers key customer insights to facilitate more informed sales decisions The ‘Outreach Everywhere’ Chrome extension lets sellers keep track of prospects from other platforms they contact them from, like Gmail and Salesforce Pricing: No info provided, contact for a custom quote. G2 reviews and rating 6. : Best AI-powered tool for Sales ops Guru Use Guru's knowledge management technology to save sales representatives from wasting time looking up information. This technology compiles important data and arranges it into a lone source of truth. Giving your sales team access to knowledge whenever they need it can help them close more deals, improve client communication, and shorten the sales cycle. For instance, an agent can find information without leaving their inbox if they need to give a testimonial by email. By giving sales representatives access to the responses to prospects' inquiries right away, this technique also gets rid of "let me get back to you on that" delays. Key features: Browser extension lets you access your knowledge base anywhere online and provides automated knowledge triggers with suggested information based on the content. Slack integration prevents data loss by allowing users to capture knowledge immediately, categorize it, and assign an expert to manage it without leaving the Slack app. Automated verification alerts prompt experts to update and verify the information. Pricing: Free options are available, $5/month/user for the Starter plan, $10/month/user for the Builder plan, and $20/month/user for the Expert plan. G2 reviews and rating 7. : Best for document/ presentation sharing and controlling Dropbox DocSend By monitoring interactions with their sales deck, DocSend makes sure that sales representatives spend their time on engaged prospects. In addition to revealing who actually viewed your deck, DocSend may offer insightful page-by-page analytics that reveal the areas on which your prospect spent the most time. By consolidating all sales-related papers in one location and making them all accessible to sales representatives and clients via a single link, this technology also removes misunderstanding. Key features: Forward tracking shows you who else is reviewing documents and influencing your deal. Update function that allows you to modify documents even after you share them so everyone is always viewing the latest version, Analytics that can be leveraged to create sales decks with a higher open/read rate. https://youtu.be/p4hzxkyn5os?embedable=true : Pricing $10/month/user for Personal plan, $45/month/user for Standard plan, $150/month/users for Advanced plan, contact for Enterprise quote. G2 reviews and rating 8. : Best searching and access capability for sales reps GTM buddy The difference between top-performing sales representatives and those who fall short of quota is typically rather large. By giving sales representatives the knowledge they need to close deals more successfully, GTM Buddy seeks to narrow this informational gap in sales performance. Based on the context of the opportunity and the sales conversation, salespeople can access the most pertinent information with GTM Buddy. Through a number of platforms, like their inbox, calendar, CRM, Slack, or LinkedIn, reps can access this data. Sales representatives may quickly respond to concerns, be well-prepared for important calls, and forge closer connections with clients by using GTM Buddy. Key features: Build customer trust and close more deals by sharing the right information at the right time. Just-in-time enablement: Find the most compelling information for your next sales call, including product knowledge, customer references, and competitive insights, right in your calendar. Better call prep: Receive real-time notifications when prospects interact with content and determine the most effective collateral at each stage of the buyer’s journey. Buyer engagement: Pricing: No info provided, contact for a custom quote. G2 reviews and rating The bottom line Every one of those products delivers its value based on the pain points of several segments in the market. so which segment of the market does your company take a place? Knowing this will help hunt your life-saving software. It’s not an easy decision that could be taken in a few days, so take your time and reach out to sales reps, and ask for presentations, personalized content, and quotas. Do your own research and ask their customers If you managed to find the perfect fit, remember to check for its integration capabilities and ease of use.

8 Sales Enablement Software for B2B Companies + How to Choose the Right One w/ Expert Insights

How AI-Powered Solutions are Shaping the Future of Marketing and Sales

Low-code MVP (Minimum Viable Product) is a basic version of a product or application made with low-code platforms. These platforms allow for fast and efficient development by reducing the amount of manual coding required, and instead relying on pre-built templates, drag-and-drop interfaces, and visual modeling options. You could build low-code MVPs to show the idea quickly and without extra spending in the first stages. Initially, low-code platforms were used by non-technical founders to create prototypes of apps. That was idea validation and mainly testing theories and features before writing actual code and doubling down on planned activities. But now, companies “to give non- or semi-technical end users, even DevOps and security engineers, the ability to easily and quickly deploy applications without the need for custom development.” use low-code or no-code MVPs Low-code development would increase by 23% in 2021 . according to Gartner Here’s a Statista 2021 report on worldwide low-code application usage where states that they use low-code for . Other use cases are . 33% of respondents data modeling and visualization data security, automating processes, and e-commerce apps The says 8 core corporate departments are now more than half dependent on no-code tools, among them: Zapier report IT operations are estimated to be 63% dependent on no-code tools in 2022. Product & development (61%) Research & development (58%). Why is it growing in popularity? There are several benefits low-code and no-code bring to the development process. Advantages of Low-Code MVPs Faster development time: Low-code platforms provide pre-built templates, components, and drag-and-drop functionality, reducing the time it takes to build an MVP. That's why the path from business ideas to ready digital products is shorter. Lower costs: Low-code platforms typically require less specialized expertise to use, reducing development costs and allowing organizations to spend resources on other areas of the business. However, that's up to business owners to use low-code tools for apps. I'd say no-code platforms haven't earned digital trust and have shown unpredicted behavior. A lot of low-code solutions need sufficient testing of the platform before deployment and checking for correct integration with other systems. Increased agility: Low-code platforms allow organizations to quickly adapt to changing requirements and pivot their MVP as needed. It takes much more time to change code than it is to choose from the list of templates. In the case of low-code development platforms, only a few lines of code could change the features. More focused development: Low-code platforms allow organizations to focus on the core functionality of their MVP, rather than spending time on time-consuming technical tasks. Coding skills can be used when there's a need for custom solutions. Better user experience: Low-code platforms typically offer pre-built UI components and templates, making it easier to create visually appealing and user-friendly MVPs. This allows developers to focus on the core functionality of the product, rather than spending time on design and development. Now, there are still rather irksome disadvantages both for the development team and potential customers. Disadvantages of Low-Code MVPs Let's be fair and state when choosing low-code MVP is not an option. Limited Customization: The same pre-built templates and drag-and-drop functionality is available for all users. So, you can see the exact copy of your website and get disappointed. This is vital for businesses that require unique and complex solutions. Dependence on Platform Provider: Low-code MVPs can create a dependence on the platform provider for updates, maintenance, and support. If the platform provider goes out of business, discontinues support, or experiences a data breach, your MVP is at risk. Learning Curve: There may be a learning curve to get familiar with the low-code platform and its features, particularly for those who are not familiar with programming. That's easier with no-code platforms, of course. Performance Issues: Applications built on low-code platforms may have performance issues when dealing with large amounts of data or a high number of users. This could be due to the of the underlying platform or the reliance on the platform provider for infrastructure and scalability. limitations Integration Challenges: Integrating low-code MVPs with existing systems and tools can be a challenge, as the platform may not provide full APIs or support for custom integrations. You'll probably need to extend or scale the MVP in the future, then it's a disadvantage for businesses with complex needs. So, before choosing low-code for MVPs, and be aware that they have many limitations. ask the right questions 5 Successful Industries With Low Code MVPs Process Automation: Low-code platforms are often used to automate business processes such as HR onboarding, expense reporting, and project management. Low-code examples of automation apps include , a travel and expense management app, and , a business process automation platform. Concur Appian Customer-Facing Apps: Low-code platforms can be used to build customer-facing apps, such as e-commerce sites, customer portals, and mobile apps. Examples of apps built with low code include , a leading e-commerce platform, and Community Cloud, a customer engagement platform. Shopify Salesforce Internal Workflow Apps: Low-code platforms can also be used to build internal workflow apps, such as HR management systems, marketing automation tools, and project management platforms. Low-code examples are , a human capital management platform, and , a marketing automation platform. Workday Marketo Rapid Prototyping: Low-code platforms are also used for rapid prototyping and testing of new app ideas. Examples of , a low-code app development platform, and , a low-code platform for enterprise app development. real-life apps built with low-code include Zoho Creator OutSystems Custom Apps for Specific Industries: Low-code platforms can be tailored to meet the unique needs of specific industries, such as healthcare, finance, and retail. a healthcare technology company, and , a banking software solution are examples built with low-code platforms. Cerner, Infosys Finacle Sum Up All-in-all, no-code and low-code tools can add standardized solutions or automate processes. However, outdated tech stack examples and security risks make them not suitable for many operations. For trying out visual interfaces, prototyping mobile applications, and developing product ideas, low-code MVPs are helpful. Feel free to discuss the topic with me ✍️

5 Successful Examples of Low Code MVPs

I've recently come across a statement in the media that the next level of business value can only be generated by bringing in non-data experts. This has caused me to reflect on data democratization and its role in unlocking this new value. I sat together (virtually) with , CEO at , to better understand the evolution and consumerization of data analytics and what the future holds for it. Amir Movafaghi Mixpanel Before joining Mixpanel, Amir served as CFO at Spiceworks Inc., an online where users can collaborate and seek advice from one another, and also engage in a marketplace to purchase IT-related services. Previously, he held various leadership roles at Twitter Inc., where, according to , he helped scale the company from 150 to 4,000+ employees and also helped lead the company through its IPO. community TheOrg.Com In our talk, we discussed why self-service analytics is here to stay as a driver of product-led growth (PLG), how new-gen SaaS companies make a difference and disrupt the user experience, how new market entrants can survive and thrive in a highly competitive environment, whether Twitter is still an amazing product, and other thought-provoking stuff. Enjoy reading! Amir, how did we achieve the level of data democratization we have now? What was the evolution? For a very long time, this world was eaten up by spreadsheets. With Microsoft Excel being dominant, you'd still have very few people in an organization that really understood and could model complex spreadsheets. And as things progressed, we went from spreadsheets to different data storages, tools, and sets of databases. We needed BI tools and SQL to join those datasets across various databases and then roll up dashboards to share with the company leadership. So, when it comes to data analysis, it remained a prerogative of data scientists and engineers for a while before a very constrained data model for marketing analytics was built and served for some time by Google Analytics and other platforms. And then, a very thin layer of functional analytics based on SaaS tools was added, which has taken up the economy over the last 10 years. What we now have left is the worst possible scenario, where we have a set of very complex tools in BI, tools that most people can't access if they want to understand data in its entirety. And then we got to the point where we had a deep level of a silo, with Salesforce serving the sales teams, Marketo serving the marketing teams – multiple functional analytics tools serving an incredibly thin level of insight into their respective functions and failing to provide the full context of the customer journey. These things are self-fulfilling prophecies when you force people into these disparate tools that don't communicate with each other. The insights people draw and how they can see their impact on business growth are limited to that narrow view. And then you have this incredible dynamic where you go to a quarterly business review, and the marketing team is celebrating; they're super happy because their local metrics are doing amazing. The product team is celebrating. Everybody is happy, but the revenue is down. And now, thanks to emerging tech, we're entering the age of event-based self-service analytics able to provide an incredible depth of insights that people with no data engineering background can easily access, leverage, and convert into value. This is an entirely new world where we get to see our impact on things that matter. Things that matter are ultimately tied to the company's outcomes in driving growth and monetization. So, is it fair to say that cloud and artificial intelligence advancements have become the main drivers of data democratization? The move to the cloud had essentially two stages. Stage one was SaaS that just copies everything on-prem and puts it on the cloud, which is Salesforce, for instance, or Workday. SaaS applications are mirror copies of their on-prem equivalents served on a cloud. We're in Stage two now, with product-led growth companies like Mixpanel, Slack, Notion or Figma taking center stage. At their core, their products innovate on user experience, workflow productivity, collaboration, and data connectivity. These tools are now defining and ushering in the new era of data democratization and disrupting "traditional" user experiences. At Mixpanel, we provide powerful self-service product analytics, allowing other companies to build better products by having a holistic understanding of how exactly people use and interact with their products. We make data accessible to anyone by generating interactive reports, letting you query your data with just a few clicks and then see visualizations in seconds. The magic of our product is really in the fact that we have a very simple, straightforward user interface that is built on an event-based model, which is the purest and most raw form of data. Interestingly, you and l process the world in the same way. We process things that happen around us or a set of actions that occur over time. And that's exactly what event analytics lets you shape, analyze, and segment. And it's incredibly generic! Everything can be modeled in this way. The simplicity and the power that event-based analytics creates are the approachability to the UX, and the ability to draw insights and build incredibly complex metrics that are very hard to compose in SQL and graph in Looker and Tableau. You can instantly generate those insights in Mixpanel with a little user learning curve. You learn a few components of what an event is, what an event property is, and who is the user that's taking actions. Then you can create cohorts, you can look at user behavior over time, you can look at retention behavior, conversion behavior – you can draw deeply intuitive insights in the same way that users think about processing the real world. That is, at its core, the future of analytics. While self-service analytics helps mitigate the silo approach, can it also help promote best practices around trust and credibility? I love that you bring that up! You can certainly self-service something, but if it's not trustworthy or credible, then there's no point and no use to it. I believe the credibility story on why data is becoming more and more reliable and why we're able to give people the ability to get to that level of accuracy has largely to do with this amazing ecosystem that has been built over the last ten years around cloud data warehousing. All the governance tools that have come to life have transformed how you can manage your data, helping increase trust and credibility. These are things that most of us wish we had a long, long time ago. After a decade of learning, we're here with all the different companies that have enabled this. Amir, how do you define product-led growth? In the enterprise world, you had this fundamental truth for a very long time that it was incredibly difficult to procure and implement on-prem software. You would sign 5-10 year contracts because of just the sheer complexity of getting something like that rolled out. As I've mentioned above, we had a gradual transition to the cloud. At first, SaaS companies took off, making it super easy for anyone to get up and running instantaneously. And while it took time for enterprise companies to slowly gravitate and realize there's a better way of doing business, a new generation of companies with innovative products in different categories emerged, allowing for a completely different dynamic of switching costs and deployment. The hurdle went down, and then right next to that, we had another cultural change from a very top-down to a very flat decision-making. In the past, all decisions were made by the C Suite. And then, there was a moment when the knowledge economy became the dominant force. As talents became the most valuable assets for any organization, business leaders asked themselves, " " " " How do we recruit and retain the best people in the industry? How can we give them better tools so they can get their job done even better? Did you know that PwC had used Lotus Notes for a very long time because IBM was a big client of theirs? So, they used the IBM product until around 2010-2011, when they skipped Microsoft Office and went straight to Google Docs. It was like skipping desktop to go to mobile – such a crazy shift. All to satisfy employees and give them more flexible and robust tools. The product has to rule. The product has to rock. Otherwise, you have no chance. Suppose you try to enter a new, incredibly competitive market. In that case, the only way you're going to win is by having a great product. So, in the enterprise, we've mentally taken ourselves away from this super legacy way of thinking and have come up with product-led growth. It's just like the consumer business. At the end of the day, users will decide whether your product is worth buying or keeping. And in the new SaaS world, there is no other way to live and survive than to have an amazing product. You can call it product-led growth; you can call it consumerization of the enterprise; you can call it whatever you want. You should have a phenomenal product with really happy end users. Period. And what makes you confident Mixpanel is an amazing product? What's your key metric? How do we know we're building an amazing product? At Mixpanel, we've reached the point when we told ourselves we're going to use Net Promoter Score as our key metric for the company. We're going to look at what our end users are telling us about how we're doing, and if we are not getting the love, then we have problems. We had to keep going and grinding, right? And we did that and went from single digits to double digits to where we're now with a consumer-grade Net Promoter Score for how our end users enjoy using the product. And that became the catalyst for product change based on what our users wanted. While analyzing user feedback, we came up with many discoveries. One of them is that no one wants gated content; no one wants to fill out a form to read a document. The same refers to pricing. After we realized users loved transparent pricing, we set ourselves off on a two-year journey to make our pricing more and more . transparent Have you ever dreamed of going into an Apple Store and negotiating the iPhone price with the salesperson? Why should you place any custom requests and go through several rounds of calls and meetings to determine the final product price? Long story short, nobody wants to negotiate. Negotiation is a means to a very bitter end. And that means that in the end, even if you got the best price, you still don't know if you got the best price. You walk away feeling like, man, maybe we could have gotten more. You walk away feeling hurt because you realize you are subsidizing the good or bad deal that other people have gotten, which is insane. So we came out and said – no more. We're going to give the best price that has the best margin integrity to everybody. We're not trying to haggle or charge this customer more than that customer. We're just going to make it an incredible buying journey, and we're going to get rid of this friction in the buying process. If you're just trying to buy the tool and get up and running, the last thing you want is this information asymmetry. You don't even know if you're going to be able to use this thing. But companies won't give you the price. Like, oh, no, we can't talk about price until our fourth meeting. And what is the most valuable thing in your life? It's your time. Give me four more hours, and I'll tell you how much it costs. We beg to differ. So after you implemented this transparent pricing system, how did it affect your sign-ups and bottom line in general? It's still early days for conclusions because we're barely out of the gate with this. Still, the hypothesis we had while making this change regarding just how much of a better conversion this would have to monetization is well validated so far in our limited data. Thanks, Amir. Now let's talk about how customers use self-service analytics tools like Mixpanel? What're the typical use cases? A few years ago, this would have been a very focused conversation around product development like, oh, Mixpanel customer XYZ did that and then changed the UX and improved the version by some crazy amount, right? That would have been the case study that I would have shared with you. But the truth is today, people are using it for all sorts of stuff. We have a recruiting funnel running in Mixpanel. BI companies use our self-service analytics in their PLG sales business. One of the largest coffee retail companies uses us for their marketing lifecycle, to get promoters and improve loyalty adoption. In addition, we have these massive tech companies where the founders use Mixpanel every day to understand their application adoption and engagement metrics. I was talking to a CFO of one of our customers the other day who said he used the leading indicators in the product data to better forecast finances and to provide those leading indicators to his investors for decision-making. That being said, self-service analytics use cases are limitless. It's powerful and it's just beginning to find that foundation. And what makes Mixpanel powerful is the order of magnitude. With cloud data warehousing taking off, adoption becomes more massive. By the way, what's the size of your social engineering team? Is it distributed? About 100 specialists today. We are fairly distributed, but mostly in the United States right now. And what are your plans for the future? Where would you like to see Mixpanel two to four years from now? My mission has always been helping other businesses scale and succeed no matter where I've worked. One thing is true – as soon as you get to a certain level of complexity, most people just don't know what's going on. Assume the company has a successful product, and it takes off. And then you make a bunch of new bets. You roll out new products, you launch new features, and then you go out and ask if they're making a difference. Are we just enjoying the same wave that took us off in the first place? Is the new stuff making a difference to us or not? And you would be shocked at just how much gap there is between awareness of what is actually making a difference and making an actual impact. Event-based analytics, in the way that we are pursuing what we're innovating on, is core power. It's that very attribution that can tie in the core events and outcomes shaping your company and tie that to what made the difference. Was it your marketing campaign? Was it the product shift? Was it some new positioning you launched? Was it a new geography that you tried to penetrate? These factors and nuances typically go into a company's monetization and growth. And thus far, all of the limitations that we talked about at the beginning of this call are really preventing companies from their ability to make smart, informed decisions about which investments to continue, which investments to stop or what new things they should go after based on an understanding of their actual revenue funnel. And that is where we're going to be. Hopefully, our vision will be realized, and Mixpanel will be able to genuinely impact how people's work directly ties into an outcome that drives the company forward. Amir, I'm also curious to learn more about your time at Twitter. So, you'd spent several years at Twitter in different roles. Could you imagine back then Twitter would eventually become what it is now? Twitter is really complicated, and part of the complication had a lot to do with its public nature. It was a closed loop because Twitter was such a live, public-facing social platform, generating a lot more opinions and public interest than even Facebook which was significantly larger and more widely adopted. In many ways, Twitter's power was indisputable in terms of what it did, how it shaped the world and how it progressed to have the impact that it still has nowadays. In the early days, we had two core paths that we were going to take to build Twitter. One was a protocol – the pipeline that fuels all the different applications that leverage this one-to-many technology. And the second way was for us to go out and own the end-user experience. And the moment we made a decision to own the end user experience had real consequences in a way that now we had to govern that experience. We had to have all of the different policies and features that have become more and more publicly relevant as the political climate has changed. From that moment on, Twitter was no longer just a technology company. It's now at the heart of how information is being exchanged, how trends are being set, and how people shape their opinions around different topics. In the early days, scaling something like this was an infrastructure problem. Now it's as much of a governance and a human problem as a technology problem. My take is that I don't particularly have any opinion that I'd like to share about the cycle of changes that Twitter has gone through from an ownership standpoint over the past years. But as long as we stay true to the real impact that not just Twitter, but all social media companies have on people and society at large, these products can be incredibly useful and valuable if we take those things seriously. Any powerful technology should also be thoughtful about how it should scale and shape to help build a better society. How we get there is more than one lane. Facebook has taken a very different path to get there than Twitter. And it's okay that there are different ways because then we'll see if one works and we can replicate it on the other. I'm glad they're not just copying each other on this route. Did you like the interview? Please share it to help more people read it.

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Artificial intelligence (AI) is rapidly transforming how businesses operate and interact with their customers. From automating routine tasks to transforming decision-making processes,  AI has the potential to significantly improve efficiency and effectiveness in the business world. In this article, I will discuss how AI is shaping the future of business and provide examples of some companies that are already implementing these technologies. AI for Automation Automation is likely to be the only major way AI will shape the future of business. This has many implications for automating business activities and affects a wide range of industries. The following are a few of the most common: 1. AI Chatbots Chatbots powered by AI are computer programs that simulate conversations with human users via text or audio. They can be used to automate customer service, answer questions, and even make recommendations, all of which were previously done by humans. Chatbots can also be used in marketing as they can be programmed to interact with customers and provide personalized services. IBM's Watson, Amazon's Lex, Google's Dialogflow, and Microsoft's Bot Framework are some of the most well-known and widely used chatbot service providers for millions of businesses. 2. Voice Recognition AI voice recognition software is also broadly used. Customer service is perhaps one of the most common applications of AI-based automation that is already in place. However, it is also widespread in voice-enabled search and navigation, controlling home automation systems, hands-free app control, and other applications. To give you an idea, Nuance's Dragon, SoundHound's Houndify, and wit.ai are some of the most extensively used voice recognition service providers. 3. Robotic Process Automation In addition to automating manual, repetitive, and time-consuming tasks, robotic process automation (RPA) products also sped up workflows considerably. Among the examples are data entry, document processing, and customer service interactions. Some of the largest companies applying it into practice are UI Path, WorkFusion, and Automation Anywhere. 4. Image Recognition Artificial Intelligence image recognition allows computers to recognize objects, scenes, and patterns in images by analyzing and interpreting them based on AI algorithms. This technology has several applications, including object detection and classification - which are typically done by humans - and facial recognition and image search. Amazon's Rekognition, Google's Vision API, and Clarifai are among the most popular and widely used image recognition services today. 5. Autonomous Mobility Autonomous mobility refers to the use of AI to enable vehicles to operate autonomously, without the need for a human driver. This technology has the potential to transform the way people and goods are transported, resulting in increased efficiency, safety, and accessibility. AI autonomous mobility is achieved through a combination of sensors, cameras, and other detectors that allow the vehicle to perceive its surroundings. The vehicle then uses AI algorithms to process this information and make decisions about how to navigate its environment, such as adjusting its speed, changing lanes, and avoiding obstacles. One of the primary advantages of AI autonomous mobility is enhanced safety. Autonomous vehicles have the potential to significantly reduce the number of road accidents by removing human error from the equation. Besides, by optimizing routes and lowering fuel consumption, the technology can help to reduce traffic congestion and improve overall driving efficiency. Another advantage of AI autonomous mobility is increased accessibility for people who cannot drive. Autonomous vehicles, in turn, can provide a new level of independence and mobility to the elderly, disabled, or those without a driver's license. This technology has been developed by Tesla, Waymo, Easy Mile, and Boston Dynamics, to name a few. AI for Data Analysis AI is also increasingly being used in data analysis to assist organizations in making better, faster, and more data-driven decisions. AI algorithms can process massive amounts of data and identify patterns and relationships that human analysts may not notice right away. This data can then be used to inform business strategies, improve operations, and stimulate innovation. Some of the more specialized applications for these tools encompass: 1. Predictive Analytics With the help of historical data, AI algorithms can predict events in the future. Using AI, companies can predict customer behavior, sales trends, and market conditions more accurately. A number of AI algorithms are embedded in Netflix and Spotify's algorithms to help the companies better understand their users' likes and dislikes. Based on this data, services can then provide relevant recommendations to their customers to keep them engaged by listening to music or watching series on those platforms. 2. Fraud Detection Large amounts of data can be analyzed by AI to detect fraudulent activities such as credit card fraud, insurance fraud, and cyberattacks. The technology can detect unusual patterns of behavior and flag them for further investigation, improving financial transaction security and protecting organizations from potential losses. Master Card has long used AI technologies to provide a fraud score for every transaction on its network as part of its anti-fraud efforts, making the information available to its issuers. Moreover, AI is used to detect fraud in combination with Mastercard's biometrics and behavioral analysis capabilities. 3. Predictive Maintenance Data from equipment and systems can be analyzed by AI algorithms to predict when they may fail. This enables businesses to take proactive measures to avoid downtime and lower maintenance costs. Every year, General Motors spends more than $1 billion on maintenance and $7 billion on capital improvements. To reduce maintenance costs and unplanned downtime, the company now uses AI-driven tools to support the widespread adoption of predictive maintenance across its over 140 global production sites[ ]. 1 4. Supply Chain Optimization AI algorithms can analyze supply chain data to identify inefficiencies and make recommendations for improvement. There are several applications for this today: to analyze data such as sales trends, consumer behavior, and market conditions to predict future demand, allowing companies to better plan their production and inventory levels. Demand forecasting – to optimize inventory management by lowering the risks of stockouts and overstocking. Inventory management - to optimize delivery routes and reduce transportation costs by analyzing data such as traffic conditions, delivery routes, and shipping costs. Delivery optimization - to provide real-time visibility into shipment status, allowing businesses to quickly identify and resolve any issues that may arise. Supply chain visibility - to analyze data such as consumer demand, shipping costs, and market conditions in order to optimize product distribution while lowering costs and increasing margins. Blue Yonder, Aera Technology, Kinaxis, and IBM Watson Supply Chain are a few of the most recognizable companies in the supply chain optimization area. Product distribution - AI for Customer Service AI is being utilized more and more to improve customer service and provide a better customer experience. Artificial intelligence technology can assist organizations in increasing the efficiency and effectiveness of their customer service operations while also providing customers with timely and personalized support. 1. Virtual Assistants AI virtual assistants can provide personalized support to customers, assisting them in finding information or completing tasks. Virtual assistants, for example, can be integrated into websites or mobile apps to assist customers with their shopping experience. As such, Walmart customers can ask Google Assistant to add specific items to their shopping carts or learn more about their shopping habits in the app[ ]. 2 2. Sentiment Analysis AI can be used to understand customer sentiments and opinions by analyzing customer feedback such as social media posts and survey responses. This data can assist organizations in identifying areas for improvement and providing better customer service. For example, the global FMCG company Mars utilizes tools to assess video content and analyze sentiment in online ratings and reviews[ ]. 3 3. Automated Voice Response AVR tools are used to provide automated customer service over the phone and interact with customers by providing audio responses to their questions or requests. These systems can be programmed to respond to simple questions and requests, provide callers with information such as current balances, allow customers to access specific account information, and, when necessary, redirect customers to an operator or customer service representative. Alvaria, Avaya, and GeneSys are three of the most recognized companies in this field. AI for Marketing AI is being used to increase the efficiency of marketing efforts in a variety of ways. AI technology can assist organizations in better understanding their customers, boosting campaign targeting, and measuring the results of their efforts. It is used in marketing in the following ways: 1. Customer Segmentation To create targeted marketing campaigns, AI algorithms can analyze customer data such as demographic information, purchase history, and social media behavior. This enables businesses to better understand their customers and increase the effectiveness of their marketing efforts. The Amazon Personalize automated segmentation system, which matches customers with the most compatible products tailored to their personal needs, is one of the most vivid examples. This is achieved through the use of special techniques that learn how users interact with items and then segment them based on their preferences. 2. Social Media Marketing To better understand customer sentiment and preferences, AI algorithms can analyze social media data such as posts, comments, and likes. This data can be used to boost social media marketing efforts, such as by creating more relevant and engaging content. Content optimization By analyzing data to uncover patterns and trends in user behavior, this technology automates the process of personalizing content to target individual users. AI-powered content optimization tools can assist marketers in creating more personalized, compelling content to engage and convert customers by understanding user preferences. This technology also assists marketers in optimizing content for various channels such as the web, mobile browsers, apps, and others. Some of the most well-known companies in this regard are Marketo Engage, BridgtEdge, and Evergage. AI for Legal work In the legal industry, Artificial Intelligence is being used to automate repetitive tasks, improve the accuracy of legal research, and provide more efficient and cost-effective services to clients. 1. Contract Review and Analysis AI algorithms can be used to review and analyze large volumes of contracts, assisting lawyers in identifying key provisions and clauses in a timely manner. When compared to manual review, this can save time and improve the accuracy of contract review. 2. Document discovery AI algorithms can be used to categorize and prioritize legal documents, such as those used in litigation discovery. This can help lawyers identify relevant documents more quickly and make more informed decisions. 3. Legal Research Legal research tools powered by AI, such as Westlaw and LexisNexis, can provide lawyers with timely and accurate information on legal precedents, regulations, and case law. This can assist them in conducting more effective legal research and providing more informed advice to clients. 4. Document Automation Artificial intelligence algorithms can be used to automate the creation of legal documents like contracts and agreements. When compared to manual drafting, this can improve the efficiency and accuracy of document creation. Kira, Ross, Cognitiv+, LawGeex, Luminance, and Legal Robot are some of the best-known tools/companies developing and selling these technologies today. AI for Creative Activities Artificial intelligence is being used in the creation of music and video to automate tasks, generate new content, and provide artists and content creators with new creative tools. 1. Music Composition AI algorithms can generate new music by either creating original compositions or recombining existing elements in novel ways. For example, AI can analyse existing music to determine the key elements of various genres and then use this knowledge to create new pieces. 2. Video Editing AI algorithms can be used to edit video footage automatically, such as selecting the best shots, removing background noise, and color correcting. In comparison to manual editing, this can save time and improve the accuracy of video editing. 3. Content Creation AI algorithms can be used to create new types of content, such as computer-generated imagery (CGI) or motion graphics. This can provide new tools for artists and content creators to create and experiment with new forms of content. 4. Music Production AI algorithms can be used to automate music production tasks like mixing and mastering. AI algorithms, for example, can be used to optimise the balance of different audio elements in a mix, thereby improving the overall sound quality of a recording. 5. Video Enhancement AI algorithms can be used to improve video content quality by removing noise, stabilizing shaky footage, and increasing resolution. This can provide tools for content creators to produce high-quality video content with less effort. Amper Music, AIVA, Flow Machines, Synthesia, Elai, and DaVinci Resolve are among the most well-known companies in this field. AI to Identify Customer Needs Last but not least, Artificial intelligence is being used to identify customer needs by analyzing massive amounts of customer data and behavior in order to gain insights into customer preferences, motivations, and needs. One of the most visible examples of this is currently taking place in the fashion industry, with some examples provided below: This online personal styling service recommends clothing items to customers based on their personal style, body type, and preferences using AI algorithms. Stitch Fix: To determine which styles and colors are most popular, the global fashion retailer uses AI to analyze data from sales, social media, and customer feedback. The data is then used to inform their design and production decisions. Zara: This online clothing rental service employs Artificial Intelligence to recommend styles to customers based on previous rentals, preferences, and events they are attending. Rent the Runway: To inform their design and production decisions, this global fashion brand employs AI to analyze consumer behavior and preferences, such as the most popular colors, styles, and materials. Tommy Hilfiger: An Indian online fashion and lifestyle retailer employs AI to recommend products to customers based on their browsing and purchasing history, as well as their preferences and style. Myntra: Conclusion To summarise everything mentioned above, Artificial Intelligence is rapidly transforming the business world and shaping the future of many industries. AI is assisting businesses in making more informed decisions, enhancing efficiency and productivity, and providing a more personalized and effective customer experience by processing and analyzing massive amounts of data. AI is used in a variety of applications, including data analysis, marketing, customer service, and legal work. In a rapidly changing business landscape, the use of AI in these areas is aiding businesses in gaining a competitive advantage and staying ahead of the curve.
Still, despite the numerous benefits of AI, there are concerns about its potential impact on the workforce, as well as on privacy and security. As AI technology evolves, it will be crucial for companies to consider these issues and take a responsible and ethical approach to AI use. Overall, AI is profoundly and far-reaching shaping the future of business, and businesses that can effectively harness its power will be well-positioned to succeed in the years ahead. Lead image developed by stable diffusion.

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