Prep for 2020 Marketing With Clean, Personalized, Predictive Data

As 2019 closes, AccuList’s data services clients have a year’s worth of multichannel customer, campaign and sales information to analyze and inform 2020 plans. So what are the big trends that the data pros foresee will deliver maximum ROI?

Data Hygiene Issues Remain a Priority

Clean, up-to-date, quality data is still the basis for good marketing analyses and campaign planning. A November Business2Community post by marketer Dan Moyle helpfully summarized the key data cleansing tasks that businesses need to undertake to hit the ground running in 2020. After all, it’s estimated that 20% of the average contact database is dirty, so this is not a trivial effort. Increasing marketing efficiency, response and customer loyalty, requires removing data errors and inconsistencies. Start by monitoring data for issues such as duplicates, missing information or bad records to figure out how and where they are occurring. Then standardize processes at each entry point. Next validate the accuracy of data across the database by investing in data tools or expert data services, and commit to regular cleansing and maintenance of data quality. Identify and scrub duplicates. Once the data has been standardized, validated and de-duped, improve its analytic value by using third-party data appending sources (to flesh out demographics, psychographics, firm-ographics, purchase history, etc.) for a more complete customer picture. Establish a feedback process to spot and update, or purge, incorrect information, such as invalid e-mail addresses identified by a campaign. And communicate standards and processes to the whole team so that they understand the value of clean data in segmentation targeting, lead response, customer service and more.

Using Data for an Agile, Personalized, Customer-Centric Edge

Data trends figured prominently in the 2019 Martech Conference and a recent article from martech firm Lineate highlights a few keynotes, such as the role of data in personalization. When a 2019 RedPoint Global survey of U.S. and Canadian consumers finds that 63% expect personalization as a standard of service and want to be individually recognized in special offers, personalized marketing is clearly a competitive essential. Expect to see use of Artificial Intelligence (AI) and machine learning (ML) increase in 2020 as personalization tools. Machine learning is when a computer is able to find patterns within large amounts of data in order to improve or optimize for a specific task. For example, for more personalized offers and messaging in acquisition, this means using ML to recognize if people from certain areas are more likely to respond to a specific offer or which past high-response special offers may resonate in future . Personalization is also key to the customer-centric experience proven to drive long-term retention and brand loyalty–as opposed to getting the same message again and again. When personalization is combined with elimination of data silos and creation of a single customer view across channels, marketing becomes especially powerful. Indeed, integrated database development and the elimination of data silos are also key to the growing “agile marketing” trend. Agile marketing breaks down team silos (which assumes breaking down data silos) in favor of teams focusing on high-value projects collectively. According to a 2018 survey by Kapost, 37% of businesses have already adopted agile marketing, and another 50% said they haven’t yet become agile but expect to be soon.  

Taking Data Insights From Retroactive to Predictive

Looking ahead to 2020, marketers should also consider adding predictive modeling to their toolkit if they haven’t already done so. Why? A study by ClickZ and analytics platform provider Keen found that 58% of marketers using predictive modeling experienced a 10%-25% ROI lift, while another 19% saw more than a 50% uplift. While retroactive campaign data can be very useful for reporting and results analysis, it’s not always as good for informing future multichannel directions, for optimizing media investments, or for quick execution and performance assessment. In fact, nearly 80% of Keen/ClickZ survey respondents felt they’d missed opportunities because of slow or inaccurate decision-making using non-predictive data reporting. For example, standard data analysis often fails to span all channels (e.g., online video vs. store-level programming) and mistakenly gives most credit to last-click channels such as search or transactional activities. In contrast, the Keen/ClickZ survey found marketers using predictive modeling boosted results in multiple areas, including a better understanding of the target audience (71%), optimizing of touchpoints on the customer journey (53%), and improving creative performance (44%). Predictive modeling also can help businesses synthesize large volumes of data, a key concern for many; in fact, 38% indicated their current measurement solutions do not support the scale of their data.