“We live in a world of 7 billion ‘me’s,” Forrester analyst Mike Gualtieri wrote in the report titled, Predictive Apps Are the Next Big Thing in Customer Engagement. “Customers increasingly expect and deserve to have a personal relationship with the hundreds of firms in their lives. Companies that continuously ratchet up individualization will succeed. Those that don’t will increasingly become strangers to their customers.”
Modern customers want engaging brand experiences that are tailored to their individual needs. Tolerance for the “spray and pray” marketing campaigns of yore is practically non-existent. A few years ago, an SUV shopper who received an email promotion for a minivan would just delete the message. Today, that same prospect will still delete the email—and then post something on Facebook about how your dealership wasted her time with an irrelevant email—and probably decide to buy her SUV elsewhere.
So what’s the answer? If you want to be relevant, you need to determine what information or offer is best suited to your customers’ needs. To understand those needs, start with predictive analytics. Predictive analytics employ a variety of techniques from statistics, modeling, and data mining to analyze current and historical customer data and develop models that predict likely preferences, future events, and next actions.
There are many types of predictive models. The most common used in the automotive industry are designed to predict the likelihood of a customer:
- Purchasing or Servicing a vehicle within a given timeframe
- Responding to an offer
- Defecting to another brand
- Advocating for your brand
- Preferring a specific vehicle class, model, feature or price point
- Spending a certain amount over their lifetime
Imagine how much more targeted, not to mention cost-effective, your campaigns might be with this information in hand. By sending fewer—but significantly more relevant communications—you will see better results.
In fact, a recent Aberdeen Group study found that campaigns based on predictive analytics resulted in an 8.3 percent incremental sales lift over control groups, and a 7.9 percent increase in click-through rates. Multiply that over several dozen campaigns, and you start to see the potential impact.
Why Use Predictive Models? So you can:
- anticipate needs
- detect preferences
- improve message timing
- increase relevance
- engender loyalty
And ultimately improve sales.