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Retailers focus on sales. The more sales you make, the more you profit. But not every sale is successful. When a customer isn’t pleased or changes their mind, they return the merchandise, sometimes slightly used, according to the retailer’s return policy. In total, returns accounted for in lost sales for U.S. retailers in 2022. There is potential revenue hidden in those numbers, but many retailers struggle to maximize the opportunities inherent in customer returns. $816 billion Amazon, the in the United States with more than 2 billion combined web visits every year, has a return strategy that can serve as a guide to other businesses wondering how to harness some of the lost revenue that comes with customer returns. knows how to handle customer returns smoothly and with reasonable profitability. most visited e-commerce platform Amazon customer service Amazon Return Policy Of course, not every customer is satisfied with the Amazon review policy. Some consumers have shared online reviews regarding challenges with getting their returns, as seen with : this Amazon shopper “...if you return anything it goes into an amazon account. It is not refunded back to the card you used to pay with! … they refused to refund my money to me.” Another customer took to X, formerly Twitter, to claims he “returned the wrong product” despite it being checked by the delivery employee, and now he faces problems: complain that Amazon “What kind of fraud is going on in Amazon,..., they are saying, I returned the wrong the product so return couldn't be initiated, Who's responsible ?” While feedback on review websites and social media tells us that not every return is handled perfectly by the online retailer, the company handles a tremendous amount of returns every day. Presumably, the Amazon return options are effective for most customers. Otherwise, there would be even more disgruntled customers leaving negative online reviews. The states that, “Items shipped from Amazon.com, including Amazon Warehouse, can be returned within 30 days of delivery, with some exceptions.” The Amazon return policy then goes on to note a few exceptions to their policy, including unique return policies by some sellers on the platform, the possibility of a different policy for Amazon Global Store, and an extended return time for certain items. official Amazon return policy While the company has found several customer-friendly options for returning Amazon items, including scanning a barcode and dropping unwanted items off to be bulk packaged at UPS stores and retailers like Kohl's, what happens next makes the Amazon return strategy particularly distinctive. How Does an Amazon Return Work? Once the customer has returned an item, the Amazon return heads back to the company to be sorted. There are several options for what happens next to these  packages: Some items purchased through individual Amazon sellers are returned directly to the seller, often traveling for long distances, but will eventually be sorted and repackaged at Amazon warehouses. Amazon packages that contain items that can be used or sold again are often sold by the company to warehouses or liquidation retailers. If an Amazon return is damaged or there are legal or hygienic concerns, the item is either recycled or used for “energy recovery.” Energy recovery turns these non-recyclable items into usable energy sources through combustion or gas recovery. Some returns also wind up being donated. Reselling Amazon Returns In some cases, items returned through Amazon customer service make their way back to customers through the official website. Goods are sent back and then sorted by the company into categories like ‘Used-like new’ or ‘Used-acceptable.’ Once graded, they are resold through certain Amazon site segments as refurbished items, warehouse deals, or overstock. Amazon Returns and Liquidation Centers Amazon has found it most profitable to not bother putting items back on the shelf. When a customer decides to return on Amazon, the item is sent back to the company. If it is new or in like-new condition, the item may be sent to an Amazon warehouse, where it could be sold at a slight discount. However, most goods that are sent back accumulate in storerooms and then are bundled into a pallet of return items. These pallets are sold to a liquidation company for a bulk price. From there, the firm will unpack the pallet, sort the things present, and list them on their official website. In this way, the liquidation company will be effectively buying bulk items – sight unseen – at a discount, then doing the work of sorting the inventory, listing the items, and selling them to customers at a profit. The Effectiveness of Amazon Return Strategy With the bulk pallet strategy used for most of the company’s returns, Amazon winds up ahead by not paying for the time, storage space, and human resources needed to sort millions of returned items individually, restock them separately on shelves, and then adjust and maintain the correct inventory numbers. Amazon accounts for physical stock when the company sells items. After that, the inventory for most items is done digitally through the return codes and software. The company has considerable savings by pushing the human resources, additional physical count, and expenses to liquidation companies. To balance the transactions and make the trade-off of labor and costs appealing to the liquidators, Amazon sells the bulk pallets at a substantially discounted price, often through an auction. A quick glance at a reviews website will tell you that Amazon doesn’t allow returns for everything customers buy. Certain items like groceries, open software, gift cards, downloads, some jewelry, and health items can’t be returned once purchased. Amazon has a well-designed strategy for satisfying customers and handling returns, but the store does have limits. As you’re designing your own return policy, it’s essential to keep that balance in mind.

The Workable Approach Behind Amazon Returns

Developer’s Mindset In Growth Projects

বৃদ্ধি প্রকল্পে বিকাশকারীর মানসিকতা

Die Denkweise des Entwicklers in Wachstumsprojekten

Mentalidad del desarrollador en proyectos de crecimiento

L'état d'esprit du développeur dans les projets de croissance

विकास परियोजनाओं में डेवलपर की मानसिकता

成長プロジェクトにおける開発者の考え方

성장 프로젝트에 대한 개발자의 사고방식

Mindset do desenvolvedor em projetos de crescimento

Мышление разработчика в проектах роста

Büyüme Projelerinde Geliştiricinin Zihniyeti

Tư duy của nhà phát triển trong các dự án tăng trưởng

开发商在增长项目中的心态

268 Stories To Learn About Project Management

Most modern DBMSs (Database Management System) are relational, i.e., they represent in the form of a two—dimensional table in which there are rows (records) and columns (fields of records). But in practice, we often come across a different data organization, namely hierarchical. data Hierarchical queries are an important tool in the field of data management, allowing you to work effectively with hierarchical data structures such as organizational trees, category trees, genealogical structures, etc. These queries play a key role in identifying relationships between data and extracting information in the form of tree structures. The problem is that data with a hierarchical structure is very poorly represented in the relational model. The SQL-92 standard does not have the means to process them. But such tools appeared in the SQL-1999 standard. However, by that time, Oracle already had its own operator. Despite this, in SQL 1999, the syntax of recursive queries is completely different from the syntax in Oracle and uses the keyword. The implementation of recursive queries in other DBMS is somewhat late, so it appeared in only in version 2005. CONNECT BY CONNECT BY WITH MS SQL Server As well as in syntax, there are differences in terminology. In Oracle, commonly discussed queries are called “hierarchical,” while all others are “recursive.” The essence of this does not change; I will use both. In this article, we will focus on the consideration of hierarchical queries in two popular database management systems - Oracle and PostgreSQL. Both platforms have unique features and approaches to processing hierarchical data, and our introduction to them will allow the reader to better understand the context of their application. A hierarchical structure can be represented as a set of nodes, where each node is connected to one or more nodes at a higher level. Important terms include parents, children, leaves, and roots. Examples of hierarchical structures include company organizational trees, family family trees, category trees in online stores, class hierarchies in educational institutions, and more. Implementation in Oracle For demonstration, we will use a test employee table consisting of 3 fields: – ID, id - is the name of the employee, name - is the ID of the manager (refers to the ID of another record in the same table) manager_id create table test_employees (
	id number primary key,	
	name varchar2(32),
	manager_id number
); Let's fill the table with test data according to the following tree: insert into test_employees(id, name, manager_id)
values (1, 'Henry Anderson', null);

insert into test_employees(id, name, manager_id)
values (2, 'Julia Miller', 1);

insert into test_employees(id, name, manager_id)
values (3, 'Mark White', 1);

insert into test_employees(id, name, manager_id)
values (4, 'Paul Scott', 2);

insert into test_employees(id, name, manager_id)
values (5, 'Daniel Hill', 2);

insert into test_employees(id, name, manager_id)
values (6, 'Maria Lopez', 3);

insert into test_employees(id, name, manager_id)
values (7, 'Monika Moore', 3);

insert into test_employees(id, name, manager_id)
values (8, 'Alex Smith', 4);

insert into test_employees(id, name, manager_id)
values (9, 'Brandon Brown', 4);

insert into test_employees(id, name, manager_id)
values (10, 'Elizabeth Jackson', 5);

insert into test_employees(id, name, manager_id)
values (11, 'Kevin Green', 6);

insert into test_employees(id, name, manager_id)
values (12, 'Jessica Johnson', 7);

insert into test_employees(id, name, manager_id)
values (13, 'Lisa Williams', 7);

insert into test_employees(id, name, manager_id)
values (14, 'Angela Martinez', 7); In Oracle, hierarchical queries appeared in version 8, long before the standard appeared. Therefore, a completely different syntax is still used. The optional operator tells the Oracle where to start the loop, i.e. which row (or strings) will be the root. The condition can be almost anything, you can even use functions or internal queries: , or , or even . START WITH manager_id is null manager_id = 1 name like 'Henry%' The operator is required. It establishes a relationship between the parent and child elements of the hierarchy. In the condition of the operator, it is absolutely necessary to use the unary operator, which refers to the previous record. CONNECT BY CONNECT BY PRIOR How does it work? Oracle finds the first record that satisfies the condition in , and starts looking for the next one. At the same time, that first entry can be accessed via . If we did everything correctly, then Oracle will search for records in which the field for storing information about the parent ( ) will contain a value equal to the id of our first record. START WITH PRIOR manager_id This way, all descendants of the root record will be found. And since the process is recursive, a similar search will continue with each row found until all the descendants are found. It is also possible to use the rownum pseudo column, in which the rows are numbered starting from 1 in the order in which they are issued. And the pseudo-column , which shows the level in the hierarchy, will also be very useful to us. So, the 1st record will have level 1, its descendants level 2, descendants of descendants - 3, etc. LEVEL select level, te.id, te.manager_id, te.name 
from test_employees te
start with te.manager_id is null
connect by prior te.id = te.manager_id
order siblings by te.id Using the keyword, we say that you need to sort only within one level of the hierarchy. This will become more clear if you remove all unnecessary fields in the request and add margins: siblings select lpad('     ', 3 * level) || te.name as Tree
from test_employees te
start with te.manager_id is null
connect by prior te.id = te.manager_id
order siblings by te.id TREE
   Henry Anderson
      Julia Miller
         Paul Scott
            Alex Smith
            Brandon Brown
         Daniel Hill
            Elizabeth Jackson
      Mark White
         Maria Lopez
            Kevin Green
         Monika Moore
            Jessica Johnson
            Lisa Williams
            Angela Martinez is a parameter for excluding cyclic references. Use this parameter along with the pseudocolumn to see which rows contain the loop. NOCYCLE CONNECT_BY_ISCYCLE Implementation in PostgreSQL We use the same test table. create table test_employees (
	id integer primary key,	
	name text,
	manager_id integer
); Queries to fill the table with data will look exactly the same as in Oracle. with recursive employees(id, manager_id, name) as (
	select te.id, te.manager_id, te.name
	from test_employees te
	where te.manager_id is null

	union all
	
	select te.id, te.manager_id, te.name
	from employees e, test_employees te
	where  te.manager_id = e.id
	)
select id, manager_id, name from employees e; Calculating a recursive query: The non-recursive part is calculated. For (but not ), duplicate rows are discarded. All remaining rows are included in the result of the recursive query and are also placed in a temporary worktable. The non-recursive part is an analog of for an oracle query. UNION UNION ALL START WITH While the worksheet is not empty, the following steps are repeated: The recursive part is calculated so that the recursive reference to the query itself refers to the current contents of the worksheet. For UNION (but not UNION ALL), duplicate rows and rows duplicating previously received ones are discarded. All remaining rows are included in the result of the recursive query and are also placed in a temporary intermediate table. The contents of the worktable are replaced with the contents of the intermediate table, and then the intermediate table is cleared. What are the other differences from the Oracle? There are no amenities like level, but they can be done on their own: with recursive employees(id, level, manager_id, name) as (
	select te.id, 1, te.manager_id, te.name
	from test_employees te
	where te.manager_id is null

	union all
	
	select te.id, e.level + 1, te.manager_id, te.name
	from employees e, test_employees te
	where  te.manager_id = e.id
	)
select id, level, manager_id, name from employees e; Recommendations for choosing between Oracle and PostgreSQL for hierarchical data The decision between Oracle and to process hierarchical data depends on a number of factors. PostgreSQL : If the data structure is a complex hierarchy with a large number of nesting levels, Oracle can provide more efficient query execution due to its optimized implementation. Data structure In the case of large enterprises with high performance and support requirements, Oracle can provide a complete set of tools and functionality to handle complex hierarchies. The scale of the project: Open source-oriented PostgreSQL may be a more flexible solution for projects that need the freedom to adapt to changes in hierarchical data. Flexibility and extensibility: In general, the capabilities of PostgreSQL are about the same as in Oracle, although, in my opinion, it is a little less convenient and transparent.

Hierarchical Queries: Comparative Analysis in Oracle and PostgreSQL

As you hopefully know from reading some of my recent articles, I really enjoy writing Kubernetes operators! I've written and also just recently about how to build them at the inaugural in Portland, Oregon. So here goes another operator-related article, although this one is about one of the later stages of operator development: performance tuning. a bit about them on this blog presented a talk FOSSY conference Since I've been working on an operator that includes multiple controllers, each with four reconcilers running concurrently that make API calls to resources outside of the cluster, I wanted to start profiling my reconciliation loop performance to ensure that I could handle real-world throughput requirements. Prometheus Metrics offers as part of its default scaffolding. This provides a great way to profile and monitor your operator's performance under both testing and live conditions. Kubebuilder the ability to export operator-specific Prometheus metrics Some of these metrics (many on a per-controller basis) include: A histogram of reconcile times Number of active workers Workqueue depth Total reconcile time Total reconcile errors API server request method & response counts Process memory usage & GC performance I wanted a way to monitor these metrics for controllers that are under development on my local machine. This means that they are running as processes outside of any cluster context, which makes it difficult to scrape their exposed metrics using a standard Prometheus/grafana setup. operator-prom-metrics-viewer So I thought, why not craft a little GUI to display this information? Inspired by the absolutely incredible , I decided to make a terminal-driven GUI using the . k9s project tview library Since I'm not (yet) storing this information for further analysis, I decided to only display the latest scraped data point for each metric. But I also didn't want to implement my own Prometheus metric parser to do this, so I imported pieces of the Prometheus to handle the metric parsing. All I had to do was implement a few interfaces for storing and querying my metrics to interop with the scrape library. scrape library itself Prometheus in-memory, transient storage The Prometheus storage and query interfaces are relatively simple, and it helps that we actually don't need to implement all of their methods! Storage The underlying storage mechanism of my class is a simple map along with a mutex for locking it during multithreaded IO. In this map, we are only storing the latest value for each metric, so there's no problem overwriting a key's value if it already exists. And we also don't need to worry about out-of-order writes or any other time-series database problems. InMemoryMetricStorage type InMemoryAppender struct {
	data map[uint64]DataPoint
	mu   *sync.Mutex
} The struct maps directly to a Prometheus metric's structure. If you've worked with Prometheus metrics before, this should look pretty familiar to you. DataPoint type DataPoint struct {
	Labels    labels.Labels
	Timestamp int64
	Value     float64
} To populate each DataPoint's key in the map, we can simply use the Prometheus builtin function to generate a unique key for each DataPoint, without having to do any extra work on our end. uint64 labels.Hash() To demonstrate, here's an example of a Prometheus metric: # HELP http_requests_total Total number of http api requests
# TYPE http_requests_total counter
http_requests_total{api="add_product"} 4633433 And how it would be represented by the struct: DataPoint d := DataPoint{
    Labels: []Label{
        {Name: "api", Value: "add_product"},
    },
    Value: 4633433,
    Timestamp: time.Now(),
} Now that we have our data model, we need a way to store s for further retrieval. DataPoint Appender Here's the interface of the storage backend appender: type Appender interface {
	Append(ref SeriesRef, l labels.Labels, t int64, v float64) (SeriesRef, error)
	Commit() error
	Rollback() error

	ExemplarAppender
	HistogramAppender
	MetadataUpdater
} Because we're building a simple in-memory tool (with no persistence or availability guarantees), we can ignore the and methods (by turning them into no-ops). Furthermore, we can avoid implementing the bottom three interfaces since the metrics that are exposed by Kubebuilder are not exemplars, metadata, or histograms (the framework actually uses gauges to represent the histogram values; more on this below!). So this only leaves the function to implement, which just writes to the 's underlying map storage in a threadsafe manner. Commit() Rollback() Append() InMemoryAppender func (a *InMemoryAppender) Append(ref storage.SeriesRef, l labels.Labels, t int64, v float64) (storage.SeriesRef, error) {
	a.mu.Lock()
	defer a.mu.Unlock()
	a.data[l.Hash()] = DataPoint{Labels: l, Timestamp: t, Value: v}
	return ref, nil
} Querier The querier implementation is also fairly straightforward. We just need to implement a single method against our datastore to extract metrics from our store. Query My solution is far from an optimized inner loop; it has awful exponential performance in the worst case. But for looping through a few metrics at a rate of at most once per second, I'm not overly concerned with the time complexity of this function. func (a *InMemoryAppender) Query(metric string, l labels.Labels) []DataPoint {
	a.mu.Lock()
	defer a.mu.Unlock()

	dataToReturn := []DataPoint{}

	for _, d := range a.data {
		if d.Labels.Get("__name__") != metric {
			continue
		}

		var isMatch bool = true
		for _, label := range l {
			if d.Labels.Get(label.Name) != label.Value {
				isMatch = false
			}
		}

		if isMatch {
			dataToReturn = append(dataToReturn, d)
		}

	}

	return dataToReturn
} Histogram implementation Since the controller reconcile time histogram data is stored in Prometheus gauges, I did need to write some additional code to transform these into a workable schema to actually generate a histogram. To aid in the construction of the histogram buckets, I created 2 structs, one to represent the histogram itself, and the other to represent each of its buckets. type HistogramData struct {
	buckets []Bucket
	curIdx  int
}

type Bucket struct {
	Label string
	Value int
} For each controller, we can run a query against our storage backend for the metric with the label. This will return all histogram buckets for a specific controller, each with a separate label ( ) that denotes the bucket's limit. We can iterate through these metrics to create objects, append them to the slice, and eventually sort them before rendering the histogram to keep the display consistent and in the correct order. workqueue_queue_duration_seconds_bucket name=<controller-name> le Bucket HistogramData.buckets The field on the struct is only used internally when rendering the histogram, to keep track of the current index of the slice that we're on. This way, we can consume buckets using a loop and exit when we return the final bucket. It's a bit clunky, but it works! curIdx HistogramData buckets for v1.0 After all of this hacking, I ended up with something that looks like this: It's far from perfect; since I don't manipulate the histogram values (yet) or have any display scrolling, it's very likely that the histogram will spill off the right of the screen. The UX can also be improved since you can only switch the controller that is being displayed by pressing the and buttons. Up Down But it's been a fantastic tool for me when debugging slow reconciles due to API rate limiting, and it has also highlighted errors for me when I miss them in the logs. Give it a whirl, and let me know what you think! Link to the GitHub: https://github.com/sklarsa/operator-prom-metrics-viewer Also published . here

Operator Performance Tuning: Building a GUI for Prometheus Metrics Visualization in Kubernetes

Now more than ever before, personalization in marketing and consumer messaging matters. Customers nowadays expect the brands they interact with to provide them with a personalized experience, as this allows them to feel seen and heard. Hyper-digitalization of has only further led to many brands increasing their online presence, allowing them to leverage new opportunities in new markets and helping to boost their engagement among loyal customers. marketing In fact, shows that personalization has become one of the most important facets of the customer journey as it helps to drive performance and further enhance the customer experience, with around 71 percent of consumers expecting brands to deliver personalized interactions, and the same percentage feeling frustrated when companies don’t provide a personalized experience. research Countless studies, surveys, and reports have shown that personalization not only leads to improved customer engagement but also helps to drive influence, boost revenue, and increase the customer conversion rate. Yet, with all this research and countless proof that indicates personalization works, some businesses are still lagging behind, leaving them in the dust, while competitors scoop a growing portion of the market share. Boosting Marketing Personalization Already in 2023, and the years before that, we’ve realized that marketing personalization can play a pivotal part in a company's success in engaging and retaining clients. Now, in 2024, two entrepreneurs, Dusty and Peter McMullin, are hoping to shed some more light on the matter of personalization, encouraging others to follow a framework that could further boost their customer retention and grow revenue. Collect Data and Analysis “Data has become the name of the game, and without it, your business is losing out on so many more valuable opportunities,” says Peter McMullin, President of Sibu Sea Berry Therapy, a natural specialty supplement and functional food brand. “There’s more to data than just numbers; in fact, we tend to think of data as our roadmap or blueprint that’s helping to guide us towards building a more personalized customer experience.” In the last several years, companies of all shapes and sizes have boosted their investment in data tools, including data harvesting and analysis tools. Using these channels, companies can gain an increased overview of who their customers are, where they’re situated, and what type of questions they’re asking or answers they’re looking for. Data collection, along with the use of Artificial Intelligence (AI) now gives a more in-depth view of what customers want from the brands they are shopping at. Data allows companies to ultimately understand customers’ pain points and the problems they’re looking to resolve. Establish a buyer persona. Now that you have the tools to collect consumer data, the second step can be to develop a buyer persona. The buyer persona should represent or be a representation of the type of people you would want to see in your target audience. requires an in-depth look at social and financial demographics. As part of this process, you’ll need to identify the touch points through which you can reach your buyers, as these can include an omnichannel of diverse areas, including social media, email marketing, in-store experiences, or other forms of messaging. Developing a buyer persona Dusty McMullin, Vice President of Operations at Sibu shares, “Developing a buyer persona gives you an understanding of the type of customers you’ll most likely be marketing for, and what are some of their likes and dislikes.” The more layers you begin to add to your buyer persona, the easier it will become for you to build a well-detailed action plan for your marketing efforts. On top of this, it would allow you the opportunity to better understand how you can craft your messaging or even further improve your personalization efforts. Segment your audiences “As a company that sells a range of niche consumer-focused products, segmentation allowed us to know what products we should be marketing to which groups of our target audience,” says Dusty. With segmentation, you begin to take another step further down the road of personalization, allowing you to further divide your audience into different groups based on various demographics, including age, gender, income, and location. By having a segmented approach, you will begin to cancel out some consumers who don’t fit your target message and further focus on those groups that provide you with more upside potential. Segmentation allows you to get behind your clients and know exactly who they are and what they’re looking for. More importantly, segmentation makes it easier for you to craft messages based on these groups’ needs and where you will need to provide customers with a point of interaction i.e., social media, email marketing, print media, etc. Get to know what your customers want By asking your customers what they want, you begin to collect more real-world data that can form part of your wider personalization marketing plan. This is simply yet another facet of data collection and is often considered a more practical approach for smaller businesses that don’t necessarily have the resources or tools to harvest and analyze vast amounts of consumer data. By asking customers what they want, you further paint a more detailed picture of your customer persona, helping to answer their most burning questions and see which areas of the business strategy and marketing campaign can be improved to help generate more engagement. Take, for example, some Search Engine Optimization (SEO) or email marketing report tools that can become valuable resources for determining what your clients are looking for, but also what are current marketplace trends. Peter shares, “In the beginning years of our business, around the time the internet was picking up steam, we had to craft emails in such a way that it deliberately asked a few simple questions, with a call to action for each.” He continues, “This allowed us to get an idea of our clients, where they may be in the customer journey, and what we can do to improve the road ahead.” Peter and Dusty recently launched a natural Omega-7 supplement that contains the proprietary T7 Sea Buckthorn Oil, created by Sibu. Sea Buckthorn has been found to hold a variety of natural properties that alleviate inflammation and boost cell production. In this scenario, we see that providing a highly specialized and niche product would require brands to develop a strategy that would enable them to better understand where clients are at in the customer journey and whether a product like this is something that will help answer their needs. Leverage social media tools If you didn’t already know, social media has a plethora of business marketing tools that can help you build a more robust marketing strategy through data collection, segmentation, and targeted ads. As a business owner, you can use social media to create a plan that focuses on meeting certain consumers at specific touch points. Although you may already have a social media marketing strategy - if you don’t already have one, start planning - taking into account which consumer segments make use of which platforms can help you further craft content messages according to their needs. People use social media for different things. Research by the found that nearly half of American social media users currently obtain their news from social media platforms, including Facebook, YouTube, Instagram, and X (Twitter). Then there are things such as gender, where research shows that more women tend to use social media platforms compared to men as a whole of total active social media users. Pew Research Center Using these platforms gives you a bird's eye view of what customers are doing on different platforms, how they perceive your messages, and what you can do to craft more engaging content, including multimedia and written content. “When our father, Bruce McMullin, founded his business back in 2004, social media wasn’t even around yet, well at least not in the capacity it is today,” says Dusty. “However, in recent years, we’ve managed to grow our brand representation and continue building new relationships with existing and potential clients through the use of social media. I think it’s mostly about our approach, but also about how we craft our messaging in such a way that we can inform customers about new product ranges or trends taking place in the industry.” Integrate Artificial Intelligence (AI) The future of online business, more specifically e-commerce, will be powered by Artificial Intelligence. Already, we’re seeing businesses across a wide range of industries and sectors bringing onboard AI tools to automate specific tasks, such as generating new social media content, analyzing customer trends, developing more advanced automated responses, and even building robust marketing strategies. Without artificial intelligence, it’s becoming increasingly difficult to navigate new challenges and obstacles in the market. More than this, there’s an entire ecosystem now being developed to help business owners find more meaning from the data they have on their customers and create personalized experiences with foolproof insights. However, while AI integration is important and valuable, the integration process thereof is just as important. You will need to that allows you to understand the basic fundamentals of artificial intelligence and what this technology can do for your business. have an AI strategy Without a clear plan about how you can implement AI in your marketing campaigns, it will leave you falling out of the pot and into the pan. Make sure that you conduct a thorough investigation of the different AI tools that are available, how they’re used, and what can benefit your business the most. Be human in your delivery Customers want to have an authentic experience more than anything, perhaps. While it’s important to build a marketing strategy around the facts and figures, letting that take charge of your business will only lead to customers feeling disengaged from your brand or your messaging. One study by revealed that around 57% of customers will likely increase their spending with a brand that they feel more connected with. Even better than this, 76% of those customers are more likely to buy from a brand, which in this case may be you, over a competitor if they feel connected with that brand. Sprout Social “People want to have real connections with a brand,” says Peter. “The limitation in this scenario is only how well you can craft a message in such a way that it makes people feel seen, heard, and noticed. Nowadays, customers want to know who’s the people behind the brand, their faces and voices. Make that part of your personalization statement.” Perhaps the takeaway here is that it’s important to be human first and then everything else. Creating a more authentic experience carries value and helps to drive customer engagement. By making this an important part of your business, you will help customers better understand the people behind the brand and the personas that are helping them find workable solutions to their problems. Wrapping Up In 2024, we can expect to see a lot more brands pushing to create authentic and personalized experiences for customers in a hyper-digital ecosystem of online shopping and social media. However, this creates a bit of a challenge for younger, less experienced entrepreneurs who are only starting their business journey. Although countless tried and trusted methods work, finding the one that perfectly fits your business niche and consumer market can help you drive the conversation and foster more meaningful relationships with your clients. “Trust the process; that's perhaps the one thing we learned when it comes to personalization marketing. Not everything will always go to plan. However, knowing that you can deliver an authentic experience that resonates with your brand and tone of voice will see you build a more meaningful connection with loyal and potential customers,” says Peter.

7 Tips To Actively Boost Your Marketing Personalization In 2024 According To Entrepreneurs  

With less than three weeks until Christmas, retailers' customer service agents are gearing up for the holiday rush, akin to athletes preparing for a high-stakes competition. The flurry of activity during this peak shopping period requires retailers to ensure they have enough staff to handle the increased number of customer inquiries, returns, and complaints to ensure the holiday season is a smoothly orchestrated event rather than a challenging obstacle course with unnecessary stress. And help is on its way. (NLP) can quickly sift through vast, unstructured data points, identifying patterns and reformatting material into formats workable for algorithms. It can automatically and efficiently process image, audio, or text-based data like emails and instant chat messages to identify consumers’ queries. Then, it uses its mighty knowledge bank to solve in seconds. Natural language processing But it’s not in every retailer’s end-of-year budget to have a communications platform overhaul. Don’t panic; it doesn’t have to be. Let’s find out how retailers can take advantage of the latest no-code chatbot solutions and boost customer satisfaction in their stores. CIO Approved: No-Code Integration Retailers’ CEOs and CIOs want the ability to pivot to innovations easily as they emerge without being locked into a single arrangement. Yet, the cost of training a “mega” large language model (LLM) like GPT-3, for example, can exceed . several million dollars As AI expenses soar, CIOs are turning to to create compact AI models tailored for specific jobs. These fine-tuned plug-in solutions offer flexibility and affordability, putting AI innovation within reach for smaller entrepreneurs. open-source software Open-source software across a consistent hybrid cloud environment simplifies building and deploying AI models. Communications providers are offering suites of AI features so that developers can plug and play with different functionalities without additional development costs so that they can find the right solution for their target market. Once integrated, retailers’ developers can continuously improve their fine-tuned models based on consumer feedback. Take , for example. It encourages users to rate the system by giving customers a feedback score, helping the tech giant enhance its product for its unique audience. Elon Musk’s Grok Assisting Customers in Peak Times One way retailers can improve customer service in peak times is to boost their website usability by making sure that relevant information is easily available. LLMs’ dual power of processing information and sifting through training data enables businesses to provide immediate, personalized support 24/7. Customers can use these features to handle basic inquiries, resolve issues efficiently, and escalate complex matters to human agents. If customers are looking for new footwear and feel overwhelmed by the options, they can turn to generative AI for assistance. Fine-tuned models can summon stores' product lines, features, and benefits and operate as customers' advisors. Responding to customers’ e-commerce queries quickly, and in a conversational manner that users can understand, builds trust and satisfaction. This helps retailers remove friction and makes processes smoother for everyone involved, ideally leading to increased sales. Supporting Customer Service Teams Behind the Scene The increasing availability of virtual assistants, thanks to no-code platforms, doesn’t mean that customers will only interact with chatbots. Rather, no-code chatbots can provide behind-the-scenes assistance, such as accessing information, summarizing correspondence, and drafting emails. Meanwhile, human support agents, particularly in smaller businesses, continue to deliver frontline contact with customers. Digging through a cluttered inbox searching for a specific note can be exasperating, mainly when email providers sort results by recency rather than relevance. It doesn’t matter how many filters and folders service teams create to help them prioritize; getting to each customer promptly takes time. Generative AI can give time back. From smart searches that can pull up any email mentioning ‘returns’ to auto-filling common responses, these behind-the-scenes assistants don’t waste a second. With instruction-tuned LLMs, the model can effectively determine which parts of the input to concentrate on before generating a response. Boosting Service Convenience and Accessibility On top of answering customer queries efficiently and streamlining operations, customer service teams can go above and beyond reaching wider audiences with text-to-audio and translation capabilities. These tools increase accessibility from voice-activated systems for on-the-go consumers to anyone who struggles with speech impairments, hearing, or sight — converting information into a format that suits them. In 2020, IBM found that use or want voice search options. AI features such as audio-to-text, transcription, and translation mean customers globally can voice their prompts or queries in their native language. The chatbots can receive and respond to their commands, reflecting the users’ communication preferences while summarizing interactions for customer service teams in transcribed notes — or visually in graphs. 69% of respondents The accessibility of no-code solutions empowers developers to build and deploy intelligent chatbots effortlessly. These neatly packaged algorithms, offered by communications providers as convenient plug-in widgets, enable computers to understand and respond to human language. This simplicity facilitates seamless integration into their company communications platforms for both developers and non-coding experts so that retailers can offer customers, and their service teams, virtual assistants at any point in their e-commerce journey.

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Worksible

worksible.com

10-50 employees

Since 2019

COMPANY RANKING

WORKSIBLE

EVERGREEN INDEX #724

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