Lead scoring has become a basic tool within the inbound marketing strategy of any retailer or e-commerce. It allows us to identify which of our leads are customers with a higher Lifetime Value (LTV) potential.
If promotions are not addressed to the right customers can be very costly or even counterproductive and make us lose customers. On the other hand, if the most receptive group of clients is made at each appropriate moment, they represent a great investment and profitable in financial terms.
The key question is how can I differentiate a good client from a bad one without hardly knowing anything about them?
In a new marketing that must necessarily be automated (marketing automation) by the volume of customers and transactions, the typical analogy of the shopkeeper who knows his clientele is as handy as not viable.
How does traditional lead scoring work?
Lead scoring is usually done by assigning an arbitrary score in each interaction with the client along the sales funnel.
For example, if the customer opens an email, he is assigned 1 point, if he clicks on the link 2 points, if he registers on our page 3, if he fills the cart with a product 4 and if it finally goes through box 5.
In this way each potential client is assigned a score according to a degree of engagement achieved. The higher the score, the more you have advanced on your way to becoming a customer.
This, let’s call it a scoring strategy, based on static rules, is automated thanks to the standard automatic marketing platforms such as Seligent, Hubspot or Marketo. These tools have become essential, but their function is none other than to automate the actions, if these actions are based on a wrong scoring, the result will not be satisfactory.
Why Machine Learning to perform lead scoring?
In the first place, this system of lead scoring rules requires that a series of interactions with the client have taken place, either through our website or through an office or contact center, in order to score it.
That is to say, we are learning if a client is interesting or not as it advances in the relationship, thanks to predictive analytics instead, we can anticipate or predict if this lead will end up being a client or not and how good it will be.
Lead scoring based on automatic data learning works differently, instead of trying to assign predefined scores based on the client’s consummate actions, what it does is learn from the patterns of our current clients that we already know in depth . According to the profile of our new client, the algorithms are able to detect patterns similar to those of our clients and grant the new lead a “scoring” according to our customer segments.
The high processing capacity of algorithms based on the science of data is always much more accurate than systems based on predefined rules. Why? Very simple, algorithms can learn from the thousands of combinations of values of dozens of customer attributes and recognize patterns in new leads with high mathematical confidence.
If we use this scoring technique to feed our marketing automation tools, we will be taking the best decisions in terms of marketing investment, multiplying the effectiveness of our actions by up to 10.
Machine Learning really works!
Hope this post has been interesting for you and looking forward to your comments and recommendations!