Predictive Analytics isn’t all maths; some of its basic concepts can deliver huge gains from any Customer Relationship Management programme.
Growth-focused SMEs, without enterprise-scale budgets, want one thing from their CRM: conversions. Performance metrics revolve around making the next call, setting the next appointment, closing that last-day-of-the-quarter sale.
And they’re suspicious of anything that gets in the way – including buzzwords like Predictive Analytics.
Should they be?
While it sounds complex, Predictive Analytics boils down to a single question:
What change in X leads to the greatest change in Y?
Substitute “sales approach” for X and “conversions” for Y, and you’ve got something Sales Directors sit up for. Imagine finding out:
- You could turn 1,000 of your coldest leads into hot prospects tomorrow.
- Conversions increase 10% when you make one change in tickle date.
- Changing one approach could leapfrog two funnel stages.
- The biggest success factor turns out to be the prospect’s growth rate.
That’s what Predictive Analytics can do. And here’s the best part: you don’t need complex software to do it, because many CRM environments have all the tools already.
Here are some ideas for using them, swinging in the power of Predictive Analytics with CRM rather than a regression analysis or a correlation coefficient.
Some of the ideas sound deceptively simple, but few companies with turnover under £50m put any resources into asking them.
Idea #1: Look for the customer’s sweet spot.
Start with this simple task: look for what unites your customers today.
Let’s say most of your customers are in food retail. You might think your natural prospects are “other food retailers”. But what if you noticed all those customers started using you when staff costs became an issue?
One chart in your CRM application – set up once, used again and again – might show how your services tend to save them two FTEs per store. That business pain (controlling headcount) has nothing to do with sector.
So if you help solve it, you’ve opened up a new area of prospecting any company where headcount is expanding faster than turnover. That’s a lot of companies.
And the best part? The information leading to that insight may be in your CRM database already.
If your sales strategy is focused on certain sectors just because that’s where your existing customers are, try looking for other descriptors that unite your roster. Looking at data across multiple criteria is a key principle of Predictive Analytics.
Idea #2: List the outside influences on your prospects.
Ask any salesperson what they want the most, and they’ll cry, “More leads!”. What if you could pull those leads from an unlikely place, such as the murky depths of the cold list?
You’ve probably got a chart that shows your drop-off at each stage of your sales funnel. But that diagram is an average. Averages hide a lot of insights.
Start by segmenting your database. (A good CRM consultant can show you how.) It can start simple: perhaps look at only sub-50-employee companies who entered your sales funnel in the last three years. (Including existing customers.)
Then look at the sales funnel for each quarter of those three years.
What if you discovered those prospects typically go cold after third tickle, unless it was around the time of their biggest annual trade event?
That insight could redraw your entire sales plan. Concentrate SME sales around the main event on their calendar. Plan your calling around it; arrange to meet them at the show; turn up and buy them lunch!
And instantly, 500 cold SMEs are back in your leads list.
That’s another key Predictive Analytics principle: outside factors that influence a decision. Salespeople want to work smart and earn the highest commission per unit of effort. So why not give them the insights to do it?
Idea #3: See what happened before the close.
Salespeople make huge efforts to talk to prospective customers but few ask how the customer wants to be contacted. This is an area where sales and marketing can truly work together.
Look at your existing customer list. At what point did they first talk to someone in sales? How long had they been in the database? How many email campaigns had they been sent? How many clicked through to the web newsletter? Let’s say all your successful wins averaged six months on the suspect list and read three newsletters before your first phone conversation.
That means if you’re calling them after the first click, you’re losing them.
But it also opens up opportunities. How many names in your database have been there six months plus and have clicked in your emails over three times? This simple Predictive Analytics method suggests they’re finally ready to talk. So call them today.
Once again, the solution doesn’t need a professional analyst on-hand – the right applications, whether in the cloud or the server closet, can show up such patterns as easy-to-understand graphs.
Idea #4: Spread the stories far and wide.
Our last idea has nothing to do with Predictive Analytics, or even with CRM. It’s about getting your people onboard the ideas above.
Once they understand how effective these methods can be, they’ll start coming up with their own ideas for more.
Imagine a marketing guy asking if he can see conversion rates comparing companies in retail with companies in consulting. He’s got an idea for two segmented campaigns that’ll help name recall when your salespeople call.
Or maybe your top saleswoman wants to know whether property developers have a similar nurturing pathway to cleaning agencies. She’s wondering if your customers’ customers are a worthwhile approach.
Everyone wants a success story. Helping them write it may take no more than a good question and a graph. (If your CRM setup isn’t capable of it, you might want to look into one that does.)