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There’s no shortage of facts making the case that data–and lots of it–is central to a successful business strategy these days. Media began using the term Big Data in 2005, and escalating gains in processing power, data storage capacity, and the ability to accumulate data on consumers with have only accelerated the volume and availability of data to marketers.
Would anyone argue that marketing in 2020 has kept pace with our ability to accumulate and process data? If more data were the answer, wouldn’t the everyday experience of consumer marketing be… better? Online ads alternate between irrelevant, offering up goods and services you don’t want or need, and creepy, stalking you long after you’ve made a decision. How many emails do you delete… each and every time you check your email? While you might like receiving an offer via text, I might not. On and on it goes, even though marketing is ostensibly smarter.
There are positive outliers, of course, but for most companies’ marketing departments, access to more data about their customers has not catapulted their marketing into the stratosphere occupied by digitally native companies (usually built on an AI infrastructure) like Netflix, Amazon, and Spotify.
Why not? Because information is not insight.
The impulse to collect more data in order to make better decisions is long-standing, if not intrinsic, to humans. From ancient Greece, to the development of the scientific method, to (yes) modern marketing, generally the first step we take to solve a problem is gathering data in a systematic way. For many marketers, that “gathering of data” means building a 360° view of the customer, that is, collecting all of an organization’s customer data in a single repository, accessible to all pertinent stakeholders, including marketing.
The data stored here might include, but not necessarily be limited to:
Many vendors in the CDP space suggest that having all the data (assuming you follow data hygiene best practices) gives marketers what they need to make better decisions. But results often don’t follow, because marketers can’t understand, much less process, the massive trove of data that even a moderate size business possesses.
Accumulating petabytes of data doesn’t produce insight on its own. Huge amounts of information are far beyond human ability to process. For an example of how humans can process information, think about the scientific method. Scientists painstakingly create experiments that isolate one or two variables to gather evidence that validates (or invalidates) a single hypothesis. They don’t randomly observe a wide array of correlated but uncontrolled events and then draw a conclusion.
What this means is that marketers, working with the wealth of data available today, often end up either facing analysis paralysis or going with their instinct: in essence, guessing. Neither of these situations result in the truly personal, relevant, timely marketing that consumers want.
A second flaw with the customer 360 without another technology for activation is the complexity of modern marketing technology stacks. While most platforms billed as customer 360s integrate well with data sources, they are not able to orchestrate and execute marketing activities. Customer data on its own (and even basic analytics of customer data) also doesn’t translate into marketing execution: deploying ads, personalizing website experiences, recommending content, or delivering social media and email messages.
On one hand, marketers have a wealth of data about their customers. On the other hand, they have a variety of execution tools that they can use to deliver highly personalized marketing experiences, content recommendations, and next best experiences. The customer 360 fills a role by aggregating data from disparate sources, but it doesn’t answer the questions marketers are asking:
Lytics refers to this gap between information and action as the decision gap.
To translate data into action, marketers need a tool that fulfills two key functions. First, it must provide technical integrations between data sources, data pipelines, and marketing execution channels. Secondly, it must possess a means to interpret data and make concrete recommendations. Given the vast quantities of data involved, the only viable means is through artificial intelligence–what we term a decision engine.
In a best-of-breed CDP stack, Lytics bridges the gap between your data and your activation channels–your website, your mobile application, your ads, social media, and emails. It applies its machine learning capabilities to your data, focusing specifically on the first-party behavioral data that underlies most customer actions, and derives insights from them.
Then, powered by data science but built for marketers, Lytics decision engine:
For many organizations, a decision engine like Lytics can function effectively on its own as a full CDP, as it can both ingest data and build holistic customer profiles. On the other hand, data pipelines, no matter how efficient, cannot provide the insights and activation that a decision engine delivers.
If you’re curious to find out if you’re ready for a customer data platform, download our CDP Readiness Guide.
