Data-driven marketing has never been hotter. With access to today's predictive and pattern analytics tools it’s entirely possible to see an increase in revenue of 30% or more. Not a bad return on marketing spend in total, and brings added bonuses such as enhancing customer experience and the insight you will gain that can translate into additional actions that further reduce cost or increase revenue. So what could go wrong?
Here are the three most common pitfalls of using big data for marketing projects:
Let’s look at each of them in a little more detail.
Regardless of how sophisticated, if you feed predictive algorithms an “un-sanitized” or incomplete data set, you’ll get incomplete or worse, erroneous results that could negatively impact the aforementioned benefits of data-driven marketing analysis.
Key Takeaway: Sending customers the wrong recommendation is much worse than not sending any recommendations at all.
As Data Scientists, we spend 95% or more “munging, wrangling, and conditioning” data. If not done properly, here are just a few basic things that could make your marketing campaigns go wrong:
Key Takeaway: Having incomplete information about a customer can be just as bad and lead to the wrong conclusion about customer profitability.
Another common error is not using data to get a “360 degree” view of the customer. Not only will this impact profitability/per customer, it may also lead to customer defections – the worst outcome as it always costs more to gain new customers than it does to retain and up-sell/cross-sell to existing ones. At a minimum, you should link customer data from the following sources:
The data may be controlled by different parts of the organization and physically reside in different databases on different servers. A lot of data is now generated on mobile devices and will only increase in the future so pay close attention to collecting data from your mobile website as well.
After collecting a complete and accurate profile of every customer, take the next step and apply several models to get insight and re-run them frequently.
Key Takeaway: plan for ongoing maintenance of predictive models (preferably daily) or they will not produce accurate recommendations.
Many marketers make the mistake of hiring consultants to build a one-time model but don’t plan for ongoing revisions of these models. The problem is that predictive analytics models for marketing (and any other predictive models) require constant tuning. The models will not continue to produce accurate recommendations unless you revise them over time. If you build models yourself, make sure to plan for the resources that will maintain the models over time.
To paraphrase a quote by Thomas Edison, “Data without action is hallucination.”
Key Takeaway: unless you use marketing analytics in your day-to-day customer interactions, you will see a zero return.
You’ll need system integration and be able to connect your customer database and predictions database directly with your marketing apps such as your call center, email service provider, direct mail house and website to get that 360 degree and accurate insight on your customers. If you use modern applications, they should have open API’s and allow for connectivity, but the process of building connectors can be tedious so make sure to ask any potential analytics tool vendor about their support for your marketing apps.
“Use it or lose it”, is the name of the game in today’s very competitive and “real-time” battle for market campaign mindshare. Just make sure you do it right!