If you read our recent blog post on the 2018 martech landscape, then you know that Scott Brinker eliminated a standalone “predictive analytics” category from this year’s marketing technology roadmap. Why?
Because predictive analytics is ingrained in nearly every piece of martech today. Widespread accessibility to big data turned predictive analytics from exclusive technology reserved only for companies with the biggest marketing budgets into something weaved into the fabric of any reputable martech stack.
Even still, a study from Nielsen shows that 35 percent of small businesses haven’t even considered using predictive analytics as part of their marketing strategy. Whether due to misconceptions about cost or time requirements, small businesses have not been as quick to take full advantage of everything predictive analytics has to offer.
Which is why we’re sharing these three predictive analytics examples for small businesses looking to take their marketing strategy to the next level:
3 Predictive Analytics Examples for Small Businesses
#1. Target Sales-Ready Customers With the Right Message at the Right Time
Mapping out the different stages of your customer journey is difficult enough. Figuring out how to identify customers at the various stages can be even harder. Luckily predictive analytics turns that work from a manual process into a data-driven, automated one. Small businesses that leverage predictive analytics can identify what a “sales-ready” customer looks like, both from a demographic and behavioral standpoint. Then, other tools in your martech stack — like your email marketing platform — can automatically tailor the messaging that goes out to that customer so as to improve their chance of converting into a new customer.
That means a higher return on investment for your marketing technology as your conversion rates improve and you’re able to bring on more new customers. It also means you can apply the same technology to your existing clientele to identify opportunities for upselling or cross-selling. But, that’s just one of the examples of how predictive analytics can help small businesses…
#2. Protect Your Baseline by Identifying Churn-Risks Early
It’s a simple fact: retaining and selling to existing customers costs significantly less than acquiring new ones. Keeping clients on your roster should be a top priority for any small business, but without the power of predictive analytics, it can often be difficult to foresee when a client is a potential churn-risk. Just as predictive analytics can tell us when a customer is most likely to make a purchase, the right solutions can also identify certain demographics or behavioral activities that indicate a client is prone to churn.
Imagine, for example, you own a locally-operated gym. Predictive analytics can identify how many visits-per-month a member needs to make to renew their membership at the end of the month. Any customers that do not hit the minimum could be targeted with emails about coming into the gym or could receive a phone call from a member of your team.
#3. Improve Your Customer Service
Small businesses looking for a way to scale customer support should look no further than predictive analytics for help. Say for example a customer looks at an article on your “Help” page about how to update their software and then calls your business shortly thereafter. The customer service representative receiving that call can quickly get information about what the customer needs and a good predictive analytics platform can easily serve up solutions.
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