3 Steps to Building a Predictive Analytics System Build a predictive analytics system when you start your business, not after you start scaling it.
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If you want your business to take off and scale, you need to embrace data from day one. Scratch that: Don't merely embrace data. Put a ring on it and shout a resounding, "I do!"
Here's why: As the landscape grows increasingly more data-centric, it's better to become data-savvy from the get-go. Think about it: The fly-by-the-seat-of-your-pants approach might work early on because of your great instincts; but what happens when you have to train a sales team?
Can you systematically give team members the instincts or judgment that you've spent decades honing? That might work in some cases, but it isn't exactly repeatable or consistent from person-to-person. Simply put, it's not an effective long-term strategy.
Ultimately, at some point, you'll need to create a systematic approach to marketing and sales. Would you rather do that right at the beginning of your business so you can grow seamlessly, or would you rather try it later on, when your overhead is higher, and you're already juggling 20 different priorities? The answer should be pretty clear.
One strong solution: a predictive analytics system that actually works. It's data-driven and based on a number of factors, so there's a consistent, repeatable way for your business to identify the best leads to pursue. That's where predictive marketing technologies come in, and can shave years off your development curve. Here's how the process looks.
1. Start with what you know -- intent.
Has a customer been to your website? Has he or she clicked on an email marketing campaign? Start with the information you know, and make sure you use it. Too many companies have tons of great information kicking around on some CRM software that they aren't using properly. Instead, use all of that information with the intention of creating a score for a lead -- a single number that makes it easier than looking up all the information separately.
YouEarnedIt, a peer-to-peer employee engagement company, offers a vast library of educational content. That content doesn't simply exist to inform an audience; it also informs them.
Using HubSpot, YouEarnedIt's marketing team is able to track visitors from their origination point to the website, building individual profiles of the ebooks, whitepapers, webinars and blog posts they interact with. This system allows the sales team to form a unique understanding of each lead's intent through specific areas and topics of interest, as well as the lead's company profile, in one streamlined system. That in turn lends itself to a more effective and informed sales approach.
2. Sprinkle in a little bit of what you don't know: predictive analytics.
Depending on whom you're working with, you could even end up with data beyond where you started. Take Mintigo as an example. When scoring leads, that company also relies on big data of its own -- meaning, financials -- to understand how much a business is spending on Google AdWords, staff, hiring, etc. This kind of data will allow your company to dial things in that much more tightly, and fill in the gaps in your own approach.
Bombora is a B2B data analytics company that pulls from a cooperative of partners, including news and commerce sites, and provides data related to a potential lead's company size and revenue, functional area, industry, professional group and seniority. Advertisers and agencies are then armed with data from outside of their owned channels that is predictive of purchase and paints a clearer path to their specific needs and intentions.
3. Target accounts and score the leads with account-based marketing (ABM).
ABM is where the magic happens, and this is why it's incredibly important to find out how your leads are being scored. Many software packages have a closed system, meaning it inputs data and churns out lead scores.
While this is certainly better than nothing, it's not ultimately what you want.
What data is behind the selection of accounts for ABM and lead-scoring models? You want to know how accounts are being selected, and leads are being scored, to make sure that the system actually works. By knowing the why behind the machine-learning models, you can target your segments, content, campaigns and even conversations with prospects to increase effectiveness and customer satisfaction.
This is the part where you can input all those instincts and wisdom and experience you've gained. Weigh key factors more strongly so you can get your team the best possible information to succeed. Think of it as copying your decision-making skills onto a computer so that your team can access those skills whenever they want without having to knock on your door to ask your opinion.
Many of the skills needed to get an idea off the ground aren't the same as those needed to scale a business from infancy to maturity.
So, in the end, you might have a great idea. You might be a fantastic marketer or salesperson by yourself. But, as you begin to add people to your team, you need to have systems in place that help your people think and work as effectively as if you were doing it yourself.
That's where a data strategy comes into play, and it's really only one of a thousand ways to systematize your business to succeed, so you can spend time working on your business and not in it.