Customer Scoring for Small and Medium Businesses — Is Now a Reality with Realomat AI
The task of any sales manager is to calculate those customers who are ready to buy and give them maximum attention. During the communication, this is done on an intuitive level, and a manager understands the degree of involvement in any dialogue. But if he/she has a large number of deals and low experience, it’s quite a challenge!

So a lot of CRMs have their own lead scoring systems to evaluate the circumstances of factors that point to a successful purchase. Also, they estimate each lead based on how many of those factors have been demonstrated so far. However, there are the most typical problems faced with these processes. Such scoring systems are not very so precise about the numbers and often give invalid information.

Tech Side: Characteristics that Help

Rate Successful Deals

So let’s look at how we decided to approach this problem more individually and find out the most important scores for our clients.

In order for the system to correctly evaluate all incoming deals, it is necessary to correctly divide the whole data you possess. We tried to develop some precise tactics for successful evaluation and let’s have look at what we get.

There are two main clusters which are shown on the pictures below: static and dynamic variables which are closely related to each other.

The static characteristics are unchanged during the agreement between the company and the client from the beginning to the end. They are, for instance, the date of creating an application (month, day), origin country, source.

The dynamic characteristics are changing at each stage of lead nurturing.

We have divided the whole process of interaction with the customer into seven stages. All of them logically go one by one. For example, “Not Done Activities” shows a number of undone actions by one manager.

So, taking into account the previous data, the system accordingly compares some leads to a certain result.

If the question about changing some characteristic arises, so you definitely can adjust any of them. From the tech side, our developers can add or remove some data. And then it will be automatically done in real time so that a sales manager always gets the relevant information.

During the development stage, we have worked out a probability square, where we checked the deals which have been already closed. Digitizing numerical and non-numerical characteristics we are better able to calculate successful and unsuccessful deals and find out why this happened.

Each stage is assigned to a certain probability. In the nutshell, the system at each stage uses data from the previous stage.

This miscalculation of probabilities depending on the stage gives a result of about 80–90%. Isn’t it impressive?

But these are just numbers and in order for a manager to get a better understanding of whether the deal is successful or not, we have developed a scale of success evaluation. It rates each case at each stage and helps easy to understand what actions should be taken for a certain deal. You can analyze the success curves on the picture below. The most prosperous deals have the estimates — А, A +, the middle passes between В and В +, and the lowest estimate is D.

scale of success evaluation

So, the manager can see the success grades of his deal at every deal stage in his CRM and this it is easier to work with the clients’ requests.

Every day our AI learns and observes lead journeys to understand their principles of functioning and develop the scoring system. Also after some time static and dynamic characteristic can be changed to the new ones by AI.

The Large Capacity of Data is Not a Problem

Possessing certain data, we can improve some information either about any employee or the whole sales department.

The most common problems that are faced by managers while working with CRM systems are lack of information, data mess, difficulties for new managers in navigation throughout their CRM systems and finding some new tasks.

Thanks to our scoring system we can find out at which stage are leads dropped throughout the sales funnel, low response rate, slow sales process, low conversion rate, etc.

In such a way the system finds out the problem and solves it like a doctor who assigns the proper treatment. This facilitates and speeds up the work of the department as previously sales managers didn’t focus on the right things approximately by 70%, incorrectly arranging their priorities.

Now it is easy to prioritize all deals, the conversion is growing and the most fascinating is that you can easily set up all activities due to our new scoring system. Besides, if it is combined with automation, the powerful synergy comes out.

And that’s not all! Now it is very convenient for entrepreneurs, or heads of departments additionally to combine everything into one personal account, to input it in the dashboard and always have a possibility to manage and control employees’ working processes.

Conclusion

Lead scoring plays an important role allowing you to set priority leads, qualify them for sales, and capture “probable transactions”.

Our scoring system helps to optimize and organize the deal characteristics for facilitating work of sales managers.

Now it is much easier to navigate throughout a large amount of data! And the business owner can control all the internal work processes the personal account.

Our scale for evaluating the deal success automatically helps a manager see the real state of a certain affair and focus on further successful deal closure.

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['Terry S.']
['Sales', 'Artificial Intelligence', 'Automation', 'Customer Service', 'Business']