The Sales department is the only one in the company that makes money. It is logical that every management seeks to maximize its productivity. One way to achieve this is with automation.
Often, automation is perceived as a magic panacea that immediately boosts all processes to the sky and allows you to dump all the problems on the machine.
But is it so? In this article, we will analyze:
Let's start with the last one.
Automation is the transfer of routine tasks from humans to robots or software. But the software performs only the set of tasks that you teach it. Therefore, automation does not directly increase metrics. It only reduces operational costs and increases the speed of a process. If the processes in the Sales department are weak, you just teach the machine the weak processes.
So, you can only automate a process when it works well in manual mode. If you can't build an analytical dashboard in Excel, it's too early to think about Power BI. You can't automate a process that you can't draw using BPMN markup. There is no need to complicate the functionality of the software if you are not happy with the MVP version.
In many Sales departments, revenue is the only metric they analyze. You made a profit — well done, everybody gets a bonus. If you didn't, everybody gets reprimanded. But this approach is fundamentally wrong, since revenue is a lagging metric. Whether it exists or not is an accomplished fact that can no longer be influenced. When you are confronted with the fact that you have not fulfilled your plan, you will only have to roast everyone in the department.
To increase the effectiveness of the department, you need to understand leading metrics. That is, the metrics by which you can tell in advance whether there will be revenue or not. It makes sense to automate the business processes that influence the leading metrics.
To do this, you need to make a list of all the metrics of your Sales department: from lagging to leading. How to make this list: take a metric and ask “What other metric affects it?” and write down the answer. Then you take the metric you wrote down and ask again what affects it, and so on, until you reach the final metric.
Here's an example:
Main metric: revenue — how much money the Sales department brings in. What affects it? Conversion — the number of calls ratio to the sales number. Conversion is high — the department is efficient. What affects conversion? Response SLA — the time in which a manager calls back the client who submitted an application on a website.
In this example, the response SLA is the final metric.
One online school conducted an experiment and measured the effect of callback speed on conversion rates. The results are impressive: if a client is called back more than 3 minutes after leaving an application, the conversion drops by 2 times. In cases of 7 minutes after submitting the application — 2 times less than the previous figure; that is, 4 times less than the original conversion.
Leading metrics may include the following:
A “hot lead” usually means a lead that already wants to buy: there is a need, desire, time, and money; a manager just needs to kindly send a payment link. But when it goes the other way, managers start crying and telling you that the leads aren't hot enough. If I hear something like that, I realize that the Sales department just doesn't work very well.
I fundamentally do not accept the concept of “hot” and “cold” leads. The “temperature” of a lead cannot be measured, the only criterion here is the manager's opinion. And this is a subjective criterion. A good manager will have a lot of hot leads, while a bad manager will complain that all the leads are cold.
Leads can only be targeted and non-targeted.
A targeted one is a lead that meets the initially agreed upon, specific criteria that distinguish it from a lead that does not fit your product.
A good example:
A Ukrainian online school sells a course called “Sales Director”. The target lead for this course is a person who works as a commercial director, sales manager, head of the Sales department, or business owner. A product manager who applied for this course is a non-targeted lead. It can be hot, cold, or whatever; one thing is important — it is non-targeted. If you failed to sell the course to him, it's bad, but not critical. But if a manager can't sell the course to a targeted lead, that's a problem.
By the way, a high percentage of targeted leads is one of the KPIs of the Marketing department of this online school. This is how the whole system works to bring in and close targeted leads.
You can automate almost anything. But in these two cases, according to experience, it is most useful.
Automation of lead validation
There are voice bots for this. It is super useful if you have an overabundance of leads. For example, you do a lot of registrations for a webinar. I worked with the bot. This is not an IVR bot that works like this: “press 1, if this; press 2, if that.” K-Call mimics a person and recognizes answers. From my experience, 97% of leads were confident that they were just talking to a manager.
I also know about the bot. According to reviews, it is even smarter; it can integrate with your CRM and set tasks for itself. For example, if a lead says: “Call me back later”, the bot finds out when the “later” is, sets itself a task for that time, and calls back at that time. Personally, I have not tested how it works yet, but I will definitely try it at some point.
Automation of processes in CRM
For example, auto-setting of tasks, auto-closing of tasks, auto-transfer by stages, sending emails or messages to WhatsApp. Such automation is done with widgets if CRM supports them, or through custom development — but it's more expensive. It saves a couple of seconds every time, but the manager's life becomes much easier.
But again, automation is not a panacea. It helps to reduce the cost and speed up already running processes. If there are no processes, or they are bad, automation won't help — fix your processes first. The main success factor of automation is transparency, specificity, and tangibility of all processes and metrics, as well as their preliminary translation into written form as instructions or regulations.