Category: Analytics

The use of analytics in the marketing, development or deployment of social games

Using machine learning to develop your hypothesis

There are many applications for machine learning, but one of the most exciting is using it to create hypothesis to test. An article in the Economist, “Computer says ‘try this’,” discusses many of the ways computers are now creating hypothesis to move medicine, farming and even cooking forward.

There are already many projects using machine learning to generate valuable and novel hypotheses. One project, BrainSCANr, suggests research topics for neuro-scientists by looking at millions of peer-reviewed papers. Research published by Baylor College of Medicine researchers used machine-learning software to review over 150,000 papers on a technique to curb the growth of cancer (proteins called kinases). The algorithm led to seven new kinases that researchers had missed. The technique is even being used to analyze search terms in Bing and Internet Explorer to determine potentially dangerous pairings of medicine.

Machine learning generates strong hypotheses

What it means for you

While machine learning is already benefitting many tech and game companies, using it to help develop hypotheses for your business is invaluable. If you are a mobile game company, think of the value of having a machine-learning system suggest that rather than focusing on your premium exchange rate, you should test changing the frequency of your free-coin bonus. Continue reading “Using machine learning to develop your hypothesis”

Changing the numbers does not change the reality

I am a huge proponent of using analytics and other metrics to drive business decisions, but I repeatedly see people making a huge and avoidable mistake. Instead of using the data to determine the best strategy, they use data to justify their intuition. A good analyst can use data to draw virtually any conclusion and if the analyst is pushed in a certain direction by the business leader, all the data does is provide people with cover for the decision rather than leading you in the optimal direction.

The same situation applies to financial analysis. I have seen people frequently manipulate numbers, often with the approval or even encouragement of the target audience, to tell the story people want to hear. I have seen this manipulation in sales, in corp dev and in internal forecasting. In all situations, it is actually just a rationale to make a decision the person already wants to make.

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Data manipulation

The first part of the problem is manipulating the data. I am not talking Enron here, but more subtly and maybe not even intentionally. People will often select the data that supports their position while discounting the other information. If you want to greenlight a certain feature, you may look at the impact on retention while neglecting the impact on monetization and rationalize it by saying it is a retention feature. Regardless of whether it is a retention or monetization, your goal is to optimize lifetime value (LTV) so you need to look at the data holistically. Continue reading “Changing the numbers does not change the reality”

Using machine learning to improve customer interaction

I recently read a very interesting post, How Machine Learning can Improve Customer Interaction, that does a great job of listing different ways you can leverage machine learning to communicate better with your customers. The ideas include:

Machine learning

  • A personalized approach when you visit a website. When you are on an e-commerce site or using a search engine, the host collects rich information on your behavior. Machine learning analyzes the data and transforms the website into something geared to the individual customer. Machine learning then will control what you see, what appears in a search bar, how the site communicates with you, to best meet your individual needs.
  • Making recommendations. Making recommendations relevant for the user was one of the first major consumer applications of machine learning. Virtually everyone has experienced Amazon’s recommendations, when you make a purchase it recommends products likely to resonate (and almost everyone has taken advantage of these recommendations). Automated personalization with machine learning takes information about the shopper, refines those recommendations and tailors them specifically to the individual shopper. As the article points out, “it is like having a salesperson with the customer the whole time, pointing out what products he or she thinks are right up the customer’s alley.”

Continue reading “Using machine learning to improve customer interaction”

Lifetime Value Part 22: The need to take a long-term view

A key to predicting and effectively using customer lifetime value (LTV) is to take a long-term view of your data and not just rely on the first month or even first few days. Many marketers will draw conclusions about a new product launch, a new feature or a unique customer cohort based on the initial data they generate. While you cannot wait months or years to make crucial business decisions, understand that these predictions are less reliable and thus making decisions based on this data is problematic.

The challenges

While intuitively more data is always better, there are challenges involved in looking back over a long period. First among these challenges is customer attribution. If you are determining the value of a specific growth channel, do you credit the lifetime spend of a user to the channel you used to acquire them initially or do you attribute the revenue to a channel (Facebook feed, email, A2U notification, etc.,) that brought the user back after a long period of inactivity.

The second issue is the sheer quantity of data. If you have millions of customers or players and years of data, it becomes quite a challenge to process all of that data. You may have multiple interactions with that user every day, literally for years. Think of how you interact with Amazon and consider they track all the products you look at, how often you visit, what you purchase, what you purchase instead, etc. You need the software, data warehousing and systems so that you can actually analyze this data quickly. Continue reading “Lifetime Value Part 22: The need to take a long-term view”