Marketing Analytics: From Data To Informed Decision

As agile marketers, we have applied cross device targeting strategy, giving brands the ability to measure for cross device attribution. Our focus has shifted from targeting the shared family computers to targeting individual consumers who are using multiple devices, at frequent intervals, across multiple channels and generating humongous amount of data. And marketers like us who are immersed with digital, are proclaiming customer data as our new master. No marketing decisions are being made without closely consulting the consumer data and related analytics. Marketing is becoming more quantifiable with data driven approach. And this is the most significant evolution in the history of marketing that helps brands to understand what data they have, what data they can get, how to organize and most important, how to activate the data. Marketers are taking active interest in technology that helps them in optimum utilization of data to make informed decisions. Analytics generated from the marketing data is acting like the bridge that is connecting the science of marketing with the art of marketing.

Due to the tracking capabilities of digital marketing, huge pool of data gets generated every day and is readily available to brands. Marketers in the recent past from progressive organisations have confessed their over indulgence with data driven marketing that consist of choosing the most important data to analyze and not wasting time on the unimportant metrics which will not bring benefits. Brands are facing the challenge of providing seamless and personalized communication across touch points and channels. In my previous articles on the importance of providing omni-channel consistency while designing customer experience, I have clarified that to provide customers with an experience that reflects their past preferences and behaviors and that anticipates what they are going to want or looking out for the next.

For personalized targeting, marketers must first understand their target audience through process of data management, analysis modelling and segmentation to send personalized offers to increase engagement and to lure spends. With different technologies and platforms at their disposal, marketers are facing new set of challenges, which did not exist earlier. From grounded in qualitative results, marketers are now being demanded by their organisations to bridge the gap between qualitative insights and quantitative results. Hence its becoming utmost important for marketers to use the data collected from each channel effectively and ultimately helpful and beneficial to the customers while having close watch on the following:

–      Making integrated decision based on available data

–      Creating the right balance between, earned, owned and paid marketing channels

–      Understand and to get right analytics to optimize marketing performance

–      Knowing customers better to adapt to their desires and demands seamlessly and instantly

–      Determining which marketing strategies and tactical moves are most successful

–      Uncovering untapped opportunities

Personalised Experience, Curated Content, Customized UI for Omni-channel Touch Points:

While omni-channel marketing is gaining prominence with every passing day, at one hand marketers are overwhelmed with data from mobile devices, social media platforms and various popular consumer interaction channels and on the other side also being presented with endless possibilities. Marketing analytics is not only helping the brands to target existing customers more effectively but also to identify and motivate potential new customers with similar metrics, efficiently. The influx of marketing analytics is giving the marketers opportunity to determine the right marketing mix and scrap campaigns that may not perform well. Marketers need to pull data from all the available channels and incorporate into every component of a marketing strategy for a seamless omni-channel customer experience. While customers are communication with brands at any point of time and from anywhere, it’s becoming complicated for marketers to achieve one-on-one engagement with everyone. On the other side of the complication, this gives enough data to marketers to obtain an accurate representation of ‘what’, ‘where’ and ‘when’; all it takes is subjective analysis to incorporate the insights gained from the data into overall marketing efforts.

Customers are becoming sophisticated and demanding more from the brands they use or follow and interacting with brands across platforms and touch points. Their expectation from the brands is that to get recognition across channels and most important delivery of precise level of timely and contextual content to them. While designing and executing campaigns, marketers are facing rapid and real time engagement from customers for sending right message out through the appropriate channels and curating the content for each touch point, wherein marketing campaign data is being collected, analyzed and used to create dynamic customer profiles and helping to determine the messages that are resonating and aren’t, so that brand messaging and campaign tactics cane be tailored for optimized results. Different TG, varied messaging preferences. Millennials may opt to engage with brands on Pinterest or Snapchat, while older audience might prefer Facebook or email communication or other traditional approach. With the internet and telecom channels, we also have to manage the traditional media channels as well. And new technology combined with historical customer and transactional data is providing better insights to marketers like us and helping is designing a more personalized customer experience. In the ever-changing dynamic omni-channel marketing landscape, audience analytics is helping brands to take advantage of the unique opportunities the data presents and to marketers who can craft targeted messages, delivered to highly segmented audiences through their preferred touch points while making a lasting impact on the target audiences’ perception of the brand and adding to the organization’s bottom-line.

Predictive Analytics Engine:

In 2014, while building the social media listening and analytics platform, I and my team realized the predictive power of data analytics engine that we were building simultaneously for the same platform. While during our preliminary research, we realized that 70% of the CMOs whom I have interviewed before building this social media listening and analytics platform, are emphasizing on the predictive analytics in their roadmap to completely data-driven marketing roadmap by the year 2020. By the time we launched the platform (Tweetrix) in 2015, I saw more CMOs and CDOs are adopting to this approach for better customer experience and for optimized marketing ROI. While more brands are facilitating interactions with their customers at a more granular level, the burden is on the marketers to use the data not only to understand the trends in the past but also to predict the future behavior. I and my team have worked with one of the leading consumer durable financing company in western India to understand the preference of their target audience (customers and prospects both) in the upcoming summer season and the aspirational brands they will aim for. The kind of insights that we have gathered from the campaign data that was targeted for omni-channel touch points were very much encouraging. Armed with the insights from the predictive analytics, the product team designed customized finance packages knowing what their customers are expecting and their aspirational product lines.

Optimisation of marketing through advanced attribution:

In marketing, attribution is the process of identifying a set of user actions (events/campaigns) that contribute in some manner to a desired outcome and then assigning a value to each of these events/campaigns. Marketing attribution provides a level of understanding of what combination of events/campaigns in what particular order influence the target audience to engage in a desired behavior, typically referred to as a conversion. Attribution provides insights into what influences the target audience, when and to what extent, allowing marketers to optimize media spend for conversions and comparing the value of different marketing channels, including earned, owned and paid, search, email, affiliate marketing, display ads, mobile medium, social media, OTT and instant chat messengers. Understanding of the entire conversion path across the whole marketing-mix eliminates the accuracy challenge of analyzing data from siloed channels. In normal practice, attribution data is used by marketers to plan future ad campaigns by analyzing various media placements that are most cost effective as determined by metrics such as effective cost per action (ECPA). While using statistical modelling and machine learning techniques to derive probability of conversion across all marketing touch points to weight the value of each touch point preceding the conversion, I have followed fractional attributes model for a long period, focusing on the Consumer choice model. Covariates X, generally includes different characteristics about the ad served (creative, size, campaign, marketing tactic, etc) and descriptive data about the consumer who responded to the ad (geographic location, device type, OS type, etc)

Customer Choice Model:

Today’s marketing attribution goes beyond delivering just accurate measurement. It is providing ‘if this-then that’ scenario planning in real time and optimized recommendations, allowing brands to drill down to results at an individual level across channels and touch points. And this is enabling marketers like us who are over obsessed with ROI to prioritize our channels more effectively and examine the role each one plays in the conversation.

Advanced attribution model provides the right value to each interaction for each customer/follower, and is providing us with an accurate understanding of our customer base as individuals to effectively target investment and engage with each individual customer in a personalized way.

Simple attribution models are not capable of effectively tracking the now-a-days multi-device and multi-channel customers with their multi-domain media consumptions and need to adapt to the advanced attribution model where marketing objectives will be more closely aligned with revenue contribution.

With the digital over indulgent world, consumers are not sticking to one channel and keep hopping across touch points. And many of them are switching between offline and online, using multiple devices. In such a scenario, data and device information can be used to figure out which channel to reach out to them and what message to disseminate. ROAS (return on ad spend) can’t be calculated accurately without making assumptions about attribution, even if it is the most fundamental metric because different campaigns perform differently during each stage of a customer’s purchase journey. For any programmatic campaign, we are using massive amounts of data and extraordinarily sophisticated algorithms to decide whether or not to deliver a message or an ad. The silo working habits of creative agencies, media buying agencies and digital services agencies are becoming passé. Now with marketing automation platforms and their advanced analytics engine, this is being fully automated for better ROI deliverables. The right marketing analytics is broadly highlighting the following for us marketers, who are taking active interest in data driven marketing:

  • Cost of acquisition
  • Percentage of converted leads
  • Percent of repeat business
  • Average total revenue per customer acquisition
  • Bounce rate (yes it still does matter)
  • Customer profiling (Buyer Personas)
  • Life-cycle management
  • Sustenance strategy

With technology advancements, we are collecting humongous amount of data than ever before and the amount getting generated via website traffic, social media, transactional behavior and other usage are growing exponentially. The challenges are increasing now-a-days, but so are the opportunities for brands and businesses to use marketing analytics and emerging technologies with techniques to mine these data. New data sources are requiring new analytical skills and marketers along with data scientists are becoming able to mine unstructured data that is available in real time streams, which is deployed with relevant communications at the appropriate and most contextual manner. Finding value proposition and actionable insights in these massive data sources is very daunting but the opportunity is being seized by CMOs and CIOs with investment is data, insight and technology and recruiting people with right skill sets to drive value from data before the peer group/competition. At one hand marketing analytics has the potential to catapult marketers and brands to newer heights, on the other hand it’s the brands and businesses need to revise the marketing and data equation by investing in infrastructure and technology and hiring right skill sets. Having a metrics driven and metrics minded team at the bottom will definitely support the success of the marketing leadership at the top, carving an essential place for marketing in any organization.

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