Simplifying customer analytics for personalized marketing
Even though most marketers understand the benefits of personalizing messages and offers to customers in marketing campaigns, surveys show many still hesitate to do so.
They understand that by offering each customer the product or products they are most likely to buy at that time, they will generate considerably more revenue. But figuring out just what to offer each customer still feels like a leap into an uncertain world.
They worry the process may be more trouble than it’s worth. The customer analytics involved seems anything but simple. Why risk changing the way they treat customers when the status quo seems to be working OK?
The fears are understandable. Some customer analytics solutions require the gathering of lots of data, integration, governance, maintenance, and then the use of advanced analytic techniques to make sense of the numbers. They are complicated and expensive.
It doesn’t have to be that complicated. When your goal is simply making marketing dollars more effective, avoid complexity. Instead, simplify!
We know from experience that complicated customer data projects, new platforms and modeling exercises are not needed to effectively personalize the marketing you’re already doing. And that can produce impressive results. Here are some simplification guidelines.
1. Focus on buying behavior. Ultimately, all your marketing dollars are spent trying to get people to buy more from you. Therefore, your most critical analytics are around buying behavior — when is each customer likely to buy next? What products are they most likely to buy? How much will they spend?
Analyzing past buying behavior to predict future buying behavior is the most accurate way to predict, and it also allows the whole analytic process to be simplified. You can now buy this kind of analytics as an affordable, automated cloud service. With it, you can personalize campaign lists that always produce more revenue per dollar invested.
When you know who’s ready to buy, what they’re ready to buy, and which customers are at risk, you have enormous power over the marketing process.
2. Do what you’re doing better. You are already doing marketing campaigns to customers. With cheap, automated analytics, you simply can get better targeted campaign lists, or lists indicating the specific messages and offers to be made to each customer that increases the probability of a purchase.
The personalized content can drop into templates with placeholders for the variable messages and offers to each customer. Most marketing automation systems easily support this and automate the execution based on your formatted list.
3. Use your own data. The customer information needed to make better-targeted marketing lists is already in your transaction files. It’s worth its weight in gold. And you already own it. Free.
4. Keep it private. You can do precisely targeted customer marketing with no privacy concerns — a good customer analytics program does not require any personal customer information.
5. Keep it simple. Avoid big data integration projects that take time and dollars are needed.
6. If you don’t know, ask. Predictive analytics will answer questions about your customer’s future behavior. When the question is, “What is each of your 10,000 customers likely to buy next month?”, that’s a job where your naturally analytic capabilities might need some help.
It doesn’t have to be complicated or expensive. It just has to be good.
This piece was originally featured in MultiBriefs.