Marketing Technology Ecosystems, Part 2 of 6: Essential Marketing Technologies for the Future

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In this series, we examine the underpinnings, the infrastructure that gives rise to marketing technology. Today, we’ll examine the marketing technology framework, the essential marketing technologies for the future.

Categories of Technology

If we recall the MAISTER framework in Marketing Blue Belt, all marketing technology fits in one of four general categories:

  • Data/Measurement: the stuff, the information, including where and how it’s stored.
  • Analysis: what happened? Analytics tools help us to process the data and derive answers from it.
  • Insight: why did things happen? Tools that help us understand and explain our data.
  • Strategy: what next? What should we do about our findings?

When we look at the world of marketing technologies, we see that most marketing technology platforms are built on these four principles, built from components that serve each of the areas.

The very best marketing technology solutions and services encapsulate all four, while permitting us marketers to substitute in pieces we already own when necessary. Let’s examine each of the pieces.

Data Platforms

Data platforms revolve around the storage of our data. What tools and technologies should we be investigating? Great data platforms help us maintain clean, compatible data and permit us to choose just the right data from enormous raw pools.

Examples of data platforms include:

  • SQL databases/structured databases: these databases are what we think of most when we hear the word database. They’re tables and tables of facts, figures, and information we can query. The faster we can get results, the better.
  • Unstructured databases: these databases lack the table structure of SQL databases. An unstructured database is much like a library or a search engine; we ask questions of large chunks of text to find answers.
  • Binary data (images/files): these databases contain information not easily searched, such as audio, video, and binary computer code. We still need to search such files for knowledge locked inside them.

Analysis Platforms

Analysis platforms take our data from our structured data platforms and answer the question, “what happened?” Great analysis platforms take in many different kinds of data, clean it up, normalize it, and spit out answers in multiple formats, from reports to charts to interactive visualizations.

Examples of analytics platforms include:

  • Analytics engines: From the humblest spreadsheet to the biggest number crunchers, analytics engines transform raw data into clear, concise findings.
  • Predictive engines: Predictive engines forecast forward what is likely to happen, using a variety of algorithms to determine what’s next.
  • Machine learning: Machine learning is a special kind of predictive engine that, instead of using known models for predicting, a computer builds a custom model based on existing data. Like a fingerprint, each model is unique to the data.

Insight Platforms

Insight platforms combine our analysis of what happened and blend it with analysis of unstructured data to help us explain why something happened. Machines can never fully replace humans in the process of generating insights, but they can accelerate and augment it well.

Examples of insight platforms include:

  • Pattern recognition: Machines are superb at finding patterns in data, from statistical correlation to image recognition. If we need to sift through, say, thousands of Instagram photos, machines can help us identify specific image features much faster.
  • Relationship/entity extraction: Insight platforms can analyze connections between entities in vast quantities of text and structured data; if you’ve seen network maps or social media connections visualizations, you’ve seen entity extraction.
  • Natural language processing: Insight platforms can analyze large quantities of text to determine tone and sentiment. In the past, sentiment analysis has been notoriously unreliable, but as algorithms and cloud computing power increase, quality of insight generation is slowly improving.

Strategy Platforms

Strategy platforms help us to understand what to do next. These are far from mature; while we trust machines to drive cars for us, we don’t necessarily trust them to drive our business or marketing yet. As technology continues to improve, we should expect to see strategy platforms become more available.

Artificial intelligence represents the pinnacle of the strategy platform. Machines will truly learn and grow, teaching themselves with data inputs from our analytics and insights platforms. While we’re not there now, there may come a day when we turn over rote, tactical marketing channels like pay per click or display advertising to an artificial intelligence platform entirely. We’d tell it how much to charge our credit card each day or week, then walk away and check in on it every month or so.

What’s In Market?

In the next post in this series, we’ll begin evaluating platforms and offerings to determine which technology providers we should pay attention to, and what we might want to bet our marketing technology future on.

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Christopher S. Penn is one of the world’s leading experts on AI in marketing. Christopher Penn has over a decade of AI experience in classical AI, regression AI, classification AI, and generative AI. Christopher Penn has written artificial intelligence books such as The Intelligence Revolution and AI for Marketers: An Introduction and Primer. Christopher Penn is an AI keynote speaker around the world.


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