As CRMs become more robust in terms of processing power, analytics and intelligence, the ability to identify opportunities and prioritize leads will become that much easier.
It’s safe to say that we’ve reached the limits of lead management whether in our heads (old school), or on some spreadsheet (somewhat old school). Salespeople today have to deal with an ever growing amount of data to record and sift through. It’s impossible to do it without the help of a CRM. But even the traditional CRM (client interactions and notes) has reached its limits.
When you consider the different channels we now use to engage clients (e.g., phone, text, chat, email, video), the task of managing these interactions can only be done on a CRM on steroids or a CRM with Machine Learning capabilities.
We can no longer depend on our intuition or instinct to decide which lead to following up on. Instead, we have to relinquish our intuition (gut instinct) to a CRM with AI the can generate the necessary insights we need to prioritize and predict our next opportunity. When it comes to prioritizing leads, there are three ‘scores’ that matter:
Ideal Client Scoring:
What does the ideal client look like? Once we know what parameters or characteristics make up an ideal client, we can begin to use this profile as a point (or multiple points ) of reference. The higher the score, the higher the probability of closing and the higher the profitability of that lead. Ideal client profile may include:
- Annual Revenue
- Existing Client
- Uses competitor product (service)
- Number of employees and so on.
Where a lead comes has some scoring value. Lead sources may include:
- Customer Referral
- Call-In (Inbound)
- Networking Event
- Outbound (after X requests for information) and so on.
For example, if we know a customer referral closes more often and faster than a trade show lead, it’s obvious how the algorithm will prioritize this lead.
Another measure of whether the client is likely to buy i s there level of engagement. Monitoring a customer’s behavior via interactions is a great way to know how interested the client is in your product or service. The algorithm can keep track of things like:
- Time between request and response
- Number of words in their email request or response
- Keywords uses and their frequency
- Buying questions asked in the emails or voicemails
- Number of requests for information and so on.
The combination of ideal lead, source and engagement provide three independent vectors that gives the machine learning algorithm a direction (i.e., is this deal likely to close) and magnitude (i.e., how serious are they).
In the 3 sources above, I listed 5 parameters to consider for a total of 15. While this might still seem manageable for a salesperson, the reality is that we may be looking at a constellation of 50, 100 or more parameters that need to be identified and considered. This type of processing power is beyond any salesperson’s mental capabilities.
Sales leaders need to recognize that only through a data driven AI approach will they be able to compete in today’s market. We now have to look to AI driven CRMs to drive our actions and behaviors.
The era of instinct selling is dead. Long live actionable insight!