In Big Data Marketing, Digital Marketing, Marketing Analytics

Personalizing the customer service with data — it’s the new gold standard of marketing. A personalized customer experience fosters loyalty and brand ambassadorship and assures a long and mutually beneficial relationship with your customer base, right? Well, yes, but let’s back up a minute. You can’t get the personalization part right if you haven’t nailed the data part. Here are the common snags when it comes to big data marketing, along with what you’re going to do to make things right.

1. The Data Only Tells Part of the Story

The data can tell you lots of things, but these are just metrics. You cannot assume that every company with an annual revenue of $6 million or more deploys ERP software, or that all businesses with a supply chain manager regularly purchase stainless steel sheets. When collecting data on your customers and leads, don’t draw conclusions based merely on raw data. Realize that within each group there is a wide range of behavior, trends, and purchase histories that aren’t necessarily what the data defines as “normal”. Personalization means getting to know your customers as individuals, not just what their metrics say they are.

2. People Lie

Unfortunately, even when you ask the right questions, you don’t always get the right answers. Some office managers might tell you they order $8,000 of office supplies each year when, in reality, they buy less than $3,000 in office supplies annually. Why they lie is a non-issue; that they lie is a given, and it’s very important in understanding personalization. Cross-check your data sets against other known factors and see what makes sense and what doesn’t. Also, when creating personalized marketing messages based on your data, allow yourself a margin of guessing about the truth.

3. People Move Among Devices and User Profiles

Sue wakes up and grabs her smartphone. She uses it to browse for gizmos on the train to work. Once she arrives at her office, she switches to her desktop computer to finish shopping. She jots down a few notes on the best gizmos to research further, but doesn’t resume her search until she’s back home that evening with a tall glass of tea and her tablet. How well can you follow Sue’s movements across these devices? You likely can’t very well. This means that you’re only working with a partial view of Sue’s shopping activities, and you might not have caught the low prices she saw on the mobile browser or the ads she was delivered on her tablet. With online lead generation and marketing personalization, you always have to assume you’re working with a partial set of the data.

4. You Can’t (Completely) Replace Humans With Algorithms

Big data

The best results are never achieved with 100% human insight or with 100% machine-driven analytics. The optimally personalized customer experience is born when humans and machines work together in concert, each using their strengths to the fullest.

When marketers get their hands on big data analysis, it’s tempting to toss all their own common sense out the window and blindly follow what the data says. The problem is, that algorithms are excellent tools for picking up on a buyer’s online habits, but algorithms are terrible at creating and delivering personalized messages to speak to these buyers. Let the machines handle the data collection and crunching, but make sure humans are handling the “reach out and touch someone” aspect of marketing.

ReachForce helps marketers increase revenue contribution by solving some of their toughest data management problems. We understand the challenges of results-driven marketers and provide solutions to make initiatives like marketing automation, personalization and predictive marketing better. Whether you have an acute pain to solve today or prefer to grow your capabilities over time, ReachForce can unify, clean and enrich prospect and customer lifecycle data in your business, and do it at your own pace.

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