5 Big Data Trends Marketers Need to Know for 2016

As marketing executives look towards 2016, shaping big data-focused strategies for customer acquisition is a key priority at many organizations. While 90% of organizations have a medium to high investment in big data, only 50% are “routinely” applying big data to regularly engage customers. Some 44% report a “lack of consistency” in omni-channel marketing campaigns. At companies with the technological basis for big data-driven marketing but a lack of consistency, targeting prospects and customers across multiple channels is likely to be an area of focus.

Tech Journalist Craig Zawada predicts that in 2016, big data will shift from a marketing priority to a “main vehicle” for driving growth in sales and revenue. As organizations increasingly adopt sophisticated personalization and targeting strategies based on first and third-party insights, organizations will likely have to choose between jumping on board these trends or risking falling behind. Join us as we review ten rising trends that data-focused marketers should pay attention to in 2016.

1. Analytics Projects Will Stop Falling Short

For many organizations, big data projects have been shaped by available data assets to date. Analysts at Tamr believe this trend will change significantly in 2016, as organizations “liberate” themselves from “artificial constraints” set by poor data asset availability or poorly-formulated business questions. Increased automation, better processes, and improved assets can allow organizations to stop participating in analytics projects that consistently “fall short” of targets.

2. Prescriptive Analytics

Traditional business analysis thought dictates three stages to analytics adoption within an enterprise:

  • Descriptive Analytics: an attempt to answer “what happened”
  • Predictive Analytics: using modeling to predict “what could happen”
  • Prescriptive Analytics: the application of simulation to make business decisions

Zawada predicts that in 2016, many organizations will reach final maturity stages and begin to apply prescriptive decision making to offer creation, targeting, and other segment-based marketing analysis. When prescriptive analytics is in full swing, an organization’s potential for marketing response can increase significantly.
3. Geo-Targeted, Programmatic Advertising

Programmatic ad targeting has introduced a new era of automation for marketers competing in the Adtech space. Consumer adoption of mobile technologies has introduced the potential for location-based targeting, which must be automated in real-time. AdTech writer Beth Principi writes that “combining location-based [big data] and programmatic” will likely have a dramatic impact on outcomes for marketers in 2016. Gaining access to location-based streams of insight on consumers will be critical for organizations who hope to take advantage of this trend.
4. Static Dashboards Will Die

CMO trust in static data dashboards is rapidly falling, according to recent research by Strata and Hadoop. As marketing executives realize the incredible potential of real-time big data, only 12% are willing to rely on static dashboard reporting for decision-making.

This statistic represents a larger shift in the way organizations are thinking about data analysis and big data. The growing preference for dynamic reporting indicates that marketers are beginning to value real-time data as a tool for the most accurate insights. Subject matter expert Bob Gourley is in agreement, writing that organizations will begin using big data to “enhance agility and…market-dominating strategies” in the year to come.
5. Mobile Takes Over

Mobile is beginning to crush social media as a digital channel for marketers. Marketing writer Michael Della Penna reports that app downloads have begun to exceed new social media followers for major brands, which creates immense potential for targeted loyalty experiences and personalization driven by big data. For companies with existing customer apps or new projects in development, big data strategies are likely to focus on:

  • Building existing customer loyalty
  • Delivering targeted offers for convenient purchase
  • Manage billing and electronic payments

App-based integration of location-based “beacon technology” will be adopted by 85% of the leading retailers in the year to come. Organizations with the ability to integrate location and multi-channel insights will uncover new potential for highly-personalized marketing messages, which offer precisely what customers need in the moment.

For organizations to remain competitive in the year to come, integrating high-quality, multi-channel data sources on consumer transactions, location, and web usage will be critical. As companies increasingly enhance their personalization efforts, organizations that drive results are likely to integrate 3rd and 1st party data sources from a Data Exchange Platform (DXP) for the most effective customer insights. Without access to the right kinds of big data, efforts to drive revenue through personalized mobile or programmatic, geo-targeted advertising are likely to be meaningless.

For more information on Big Data Exchange’s marketplace for real-time data insights, click here.

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Do You Know Your Buyers? 3rd-Party Data Insights for Existing Customer Conversions

Marketers have long understood that it’s easier to sell to your existing customers. However, research increasingly indicates that effective personalization is necessary for repeat sales. 74% of online customers get “frustrated” by product recommendations that are irrelevant to their needs and interests. Data consistently indicates a lift in sales when personalization segments and affinity analytics are effectively applied to online marketing. However, for brands who lack Amazon’s envious average customer lifetime value from effective personalization, the problem could be your insights.

 

Some 52% of marketers consider personalization fundamental to successful marketing, but only 20% of marketers use behavioral triggers. The ability to generate personalized offers, deals, and recommendations when an existing customer begins searching can ensure you’re best able to capture repeat sales in micro-moments of need.


As marketers shift towards more sophisticated applications of analytics for personalized marketing, the need for better segmentation and real-time analytics is clear. Developing a strategy for earning repeat spend from your existing contacts can have a significant impact on your revenue. Generating effective product recommendations and offers that mirror your prospect’s real needs in the moment requires access to transactional, quantitative, and qualitative insights from a variety of sources

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What Does Effective Personalization Look Like?

 

There’s no question that creating personalized customer experiences can allow brands to increase retention and loyalty among repeat buyers. Adobe refers to “increasing data availability” as “disruptive,” for brands with the savvy to know their existing buyers better than ever before. Per Echidna, the status quo for effective customer personalization involves:

  • Optimal timing of personalized product recommendations and offers
  • Sophisticated adaptation based on demographics, “behavioral, and transactional” insights
  • Multidimensional recommendations, across categories, brands, products, and offers
  • Multi-channel personalization, for desktops, smartphones, and mobile devices

 

Brands must look beyond their first-party transactional insights to develop comprehensive understanding of their customers. By leveraging a variety of big data insights from social media, third-party transactions, and demographics, it’s possible to gain better insight into your customer’s latest preferences and purchases. Best-of-class marketers understand the importance of generating offers and recommendations based on searches your customers performed on their mobile device minutes ago, not a purchase made on your website last year.

 

Big Data Insights for Effective Personalization

 

Despite the critical importance of big data-driven personalization for digital marketing, adoption lags across industries. 26% of organizations have yet to adopt any real-time insights to personalize web experiences, and 39% of marketers believe their data is too old to produce actionable insights. While the average organization has yet to tap into 95% of their data assets, improving your utilization of internal transactional insights probably isn’t the key to better personalization.

 

However, simply integrating third-party data insights into your personalization strategy won’t necessarily yield improved results. In a case study of Kraft, the brand achieved approximately 50% accuracy with third-party data assets. Further analysis by Kraft’s digital marketers revealed the issue was with their data vendor’s segments. Their vendor classified women as “trendy” if they visited a high-end furniture store website within the previous six months.  The experience lead Kraft VP Bob Rupczynski to conclude “There’s just so many problems with the quality and the recency…third-party data has a problem.”

 

Will Integrating Third-Party Data Insights Improve My Personalization Efforts?

 

For many brands, taking steps to integrate third-party data insights can provide much richer grounds for personalization. However, not all data vendors are created equal. To avoid similar results to Kraft’s by purchasing from a vendor with significant quality and recency issues, look for a Data Exchange Platform (DXP) to provide real-time, quality scored data and consider the following criteria before integrating any third-party insights into your marketing campaigns:

  • Are we renting this data, or is it an owned asset? Marketers typically maintain better control over segmentation and classification of owned assets.
  • Can these insights be integrated? The right data vendor will allow you to combine third-party assets with your own CRM insights conveniently in a data management platform (DMP).
  • What don’t we know about our customers? Chances are, your own data assets lack understanding of your customer’s interactions with other brands, demographics, and preferences. Seek out data vendors that allow you to fill in the gaps.

 

The BDEX Data Exchange Platform (DXP) allows marketers to access the right insights to improve personalization instantly across hundreds of quality scored data sources. Instead of fighting to generate relevant offers from six month-old web browsing history, you’ll gain access to minutes-old data from third-party vendors. Once purchased, marketers own the knowledge assets for seamless integration in personalization strategies.

 

For more information on the importance of personalization, we recommend our recent blog Why Personalized Mobile Marketing isn’t Possible Without Big Data.

 

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