Game Changer: Big data in marketing analytics
By Mousumi Ghosh
Big data is the biggest game-changing opportunity and paradigm shift for marketing since the invention of the phone or the Internet going mainstream. Big data refers to the ever-increasing volume, velocity, variety, variability and complexity of information. For marketing organizations, big data is the fundamental consequence of the new marketing landscape, born from the digital world we now live in. The term “big data” doesn’t just refer to the data itself; it also refers to the challenges, capabilities and competencies associated with storing and analyzing such huge data sets to support a level of decision-making that is more accurate and timely than anything previously attempted: big data-driven decision-making.
Organizations today face overwhelming amounts of data, organizational complexity, rapidly changing customer behaviors and increased competitive pressures. New technologies, as well as rapidly proliferating channels and platforms, have created a massively complex environment. Data worldwide is growing 40 percent per year, a rate of growth that is daunting for any marketing and sales leader. Many marketers may feel like data has always been big – and in some ways, it has. But think about the customer data businesses collected 20 years ago – point of sale transaction data, responses to direct mail campaigns, coupon redemption, etc. Then about the customer data collected today – online purchase data, click-through rates, browsing behavior, social media interactions, mobile device usage, geolocation data, etc. Comparatively speaking, there’s no comparison.
Having big data doesn’t automatically lead to better marketing. We can think of big data as a secret ingredient, raw material and an essential element. It’s not the data itself that’s so important. Rather, it’s the insights derived from big data, the decisions we make and the actions we take that make all the difference.
Three types of big data are key for marketing:
1. Customer: The big data category most familiar to marketing may include behavioral, attitudinal and transactional metrics from such sources as marketing campaigns, points of sale, websites, customer surveys, social media, online communities and loyalty programs.
2. Operational: This big data category typically includes objective metrics that measure the quality of marketing processes relating to marketing operations, resource allocation, asset management, budgetary controls, etc.
3. Financial: Typically housed in an organization’s financial systems, this big data category may include sales, revenue, profits and other objective data types that measure the financial health of the organization.
Having big data doesn’t automatically lead to better marketing
Organizations that want to succeed in marketing should do the following things well:
1. Successful discovery of new opportunities. Successful discovery requires building a data advantage by pulling in relevant data sets from both within and outside the company. Relying on mass analysis of those data, however, is often a recipe for failure. Analytics leaders need to go beyond broad goals such as “increase wallet share” and get down to a level of specificity that is meaningful. They need to use digital information to better target buyers and use heaps of analytics to learn more about target buyers than ever known before. Modern marketers should shed light on a more granular level of detail, such as: which websites a user frequents most often, which social media profiles they have and use, and even which buttons they click on a given website. The “ideal customer profiles” can easily be targeted with the big data.
2. Understand consumer decision journey. Today’s channel-surfing consumer is comfortable using an array of devices, tools and technologies to fulfill a task. Understanding that decision journey is critical for identifying battlegrounds to either win new customers or keep existing ones from defecting to competitors. Marketing and sales leaders need to develop complete pictures of their customers so they can create messages and products that are relevant to them.
By combining big data with an integrated marketing management strategy, marketing organizations can make a substantial impact in these key areas:
- Customer engagement: Big data can deliver insight into not just who your customers are, but where they are, what they want, how they want to be contacted and when.
- Customer retention and loyalty: Big data can help you discover what influences customer loyalty and what keeps them coming back again and again.
- Marketing optimization/performance: With big data, you can determine the optimal marketing spend across multiple channels, as well as continuously optimize marketing programs through testing, measurement and analysis.
3. Monitor Google Trends to inform your global/local strategy. Google Trends is probably the most approachable method of utilizing big data. Google Trends showcases trending topics by quantifying how often a particular search-term is entered relative to the total search-volume. Global marketers can use Google Trends to assess the popularity of certain topics across countries, languages or other constituencies they might be interested in, or stay informed on what topics are cool, hip, top-of-mind or relevant to their buyers.
4. Create real-time personalization to buyers. Marketers need to send the right message at the right time. Timeliness and relevancy aren’t just qualities of the Fourth Estate; they’re also the foundation of successful marketing campaigns, e-mail click-through rates and consumer engagement with your brand.
Big data gives marketers timely insights into who is interested or engaging with their product or content in real time. Tying buyer digital behavior into your CRM systems and marketing automation software allows you to track the topics that your buyers are most interested in and send them content that makes the most sense to develop those ideas or extrapolate on those topics.
5. Identify the specific content that moves buyers down the sales funnel. How successful was a singular blog or social post at generating revenue? Before big data that was an unanswerable question. We executed on social media strategies and content creation because we had a feeling that it was working, but we had no way to back that claim. Now, marketers can distill the effectiveness of a marketing push down to tweet. Tools like content scoring illuminate which individual content assets were successful to a closed/won deal, and which were inefficient. This allows marketers to hone the strategies around the content topics or types that resonate with their buyers the most, and truly compel them to purchase.
6. Make it quick and simple. Companies need to invest in an automated “algorithmic marketing,” an approach that allows for the processing of vast amounts of data through a “self-learning” process to create better and more relevant interactions with consumers. That can include predictive statistics, machine learning and natural language mining. These systems can track key words automatically, for example, and make updates every few seconds based on changing search terms used, ad costs or customer behavior. It can make price changes on the fly across thousands of products based on customer preference, price comparisons, inventory and predictive analysis.
Mousumi Ghosh (email@example.com) is a vice president and senior manager of pricing analytics at JPMorgan Chase & Co. where she works with regional sales, credit teams and senior management. She is a member of INFORMS.