The accuracy challenge in marketing analytics

With the unimaginable capabilities of big data analytics, marketing organizations just can’t seem to get enough. Data is expected to grow to 175 zettabytes by 2025, but the more they collect, the more they want. But despite being submerged by the sheer growth in the volume, velocity, and variety of the data, they struggle to derive value – because much of this data is far less accurate than they expect it to be. This inaccuracy makes it extremely difficult (if not impossible) to understand the needs of current and potential customers, and the efforts needed to develop more aintimate, meaningful relationship with them.

The problem with Big data that’s both incomplete and inaccurate

Despite the accepted capabilities of marketing analytics, the issue of inaccuracy is far too common. Outdated or incomplete information, modeling errors, wrong sampling of data, poor data governance strategies, and data corruption are just some of the reasons why analytical data turns bad.

The impact of wrong big data analytics can be extremely far-reaching —especially for marketers who make important decisions based on this incorrect information.

  • The biggest problem with inaccurate analytics is missed opportunity. The goldmine of important information you are sitting on can be used to send timely, relevant, and value-driven messages to your customers – instead you fail to drive marketing effectiveness while depriving customers of what they need.
  • Inaccurate understanding of consumer spending behavior can substantially impact business decisions. Not only will you not know how much consumers are willing to spend in the future, but also what types of products they are likely to purchase.
  • Wrong predictions on how much revenue your company can expect to see in the future can result in wasted cross-selling and up-selling efforts.
  • Incorrect analysis can mean dropping customer loyalty and revenue; if you cannot use the data you produce in the right manner, you run the risk of falling short of customer expectations – and providing opportunities foryour competitors to sweep them off their feet.
  • Improper analysis can result in moving customer relationships too far too soon. Even for customers who are still getting to know you as a company, sending too many personalized emails can be a little too needy for comfort, and can destroy the relationship you’re trying to cement.
  • The wrong analysis can also mean your personalized messaging is full of pitfalls; you might address your customers by the wrong name (or title), offer insurance for a new car they never bought, or send congratulatory messages for a baby they never had!

Prescription for success

For marketers who increasingly depend on data to guide business decisions and pursue personalized marketing strategies, the dangers of inaccurate data range from inconsequential embarrassments to complete customer hostility. Add to that the complacency driven by overconfidence in the accuracy of data.This can compromise marketing efforts to an extent where it undermines the overall strategy.

Since data today plays a much greater role in how marketing strategies are developed, here’s a list of some things you can do to overcome the accuracy challenge in marketing analytics:

  • Demand more accountability, transparency, and communication from your data collection teams; ask for details about how data is collected, what validation methods are being used, and the dependencies and assumptions, if any.
  • Never rely on just a limited number of data points; always consider the largest possible segment for understanding customer behavior. As for personalized messaging, limit the geographies and scope for higher accuracy.
  • Have a robust data governance strategy in place to define how data will be collected, stored, managed, archived, and backed up across the organization.
  • Always look for inaccuracies in existing data sets, verify changes being made to analytical models, and understand the rationale behind these changes.
  • Use analytical models that ensure data relevance; make sure your algorithms only output data that is relevant, thus allowing you to make the right decisions.
  • Make data security an integral part of your marketing organization; implement the right data protection programs, carry out frequent data privacy audits, and take the right security measures to avoid breaches while being compliant with evolvingregulations like GDPR.
  • Always know where your data is coming from: ascertain the source, as well as the provenance; have measures in place to maintain adequate control over data accuracy, and transparency regarding data sources.
  • Make sure to test data samples for inaccuracies or inconsistencies against data fields you already have or can validate.
  • Continually assess your data sources, and evaluate the efficiency of your data collection processes, analytical methodologies, models, and assumptions; if necessary, take feedback from customers, and use their inputs as a reference for your analytical models to enhance personalization efforts.
  • Understand that any big data analysis is prone to human error; make sure to run verification tests to ensure your information is accurate, timely, and valuable.
  • Lastly, beware of an over-reliance on analytical data. Never trust the outcomes blindly; complement the decisions derived from big data with your own insights based on experience.

Marketing analytics opens a sea of opportunities for the modern enterprise, but it also has its share of limitations. Understanding these limitations and working towards overcoming them is the only way to ensure marketing effectiveness and strengthen customer relationships and trust that you work so hard to build and sustain.