Like tapping a newly found oil well, companies everywhere are in hot pursuit of creating value from data analytics. In 2014, data analytics and business intelligence companies received the largest amount of venture capital of any technology segment, representing 10% of total VC investment in Q4 2014 alone.
Data analytics isn't just a new company phenomenon. Given my previous experience running a data analytics company, I have been introduced to and recently met with several companies looking to add analytics to their existing product offerings. In some cases these companies have completely changing their focus to becoming "an analytics company." Typically, these companies already provide a cloud-based software or other product and are making the leap to provide greater customer value through analytics. Whether these companies are generating data themselves through their software or IoT-like sensors or other devices, or they are able to tap into another data source, they are looking to maximize their value by creating new customer insight via analytics.However, the road to how to incorporating analytics into an existing product line is not always clear as there are multiple strategies for creating product and company value. Companies like Netflix and Amazon have used analytics, in the form of a recommendation engine, to enhance their product offering, which is an effective strategy. Startup unicorn Square has demonstrated how analytics can create additional, reinforcing customer by enhancing its core business offering, as a transaction and payments engine, that differentiates it from other platforms.Still others, like my last company, PublicEngines (now part of Motorola) took the path of creating a new service offering that was separate and distinct from our other products. In our case we created a predictive analytics platform that took advantage of data that we already has access to and reused it to create additional customer value, and we commanded a subscription premium for it.
No matter what your plan, if you are looking to enter the analytics market or adding analytics to an existing platform, here are a few things to think about to help you plan out your approach:
- Think game changer. Don't think about analytics just as a product or product feature alone. Instead, make analytics an inimitable strength for your strategic position and create additional barriers to entry for your company. In the software world especially, playing fields are too level, barriers to entry are too low to not create as much advantage as possible. So, find a way to use analytics as a strategic asset. For example, in my last company, we discovered that much of the data that we used, which was generated by the customer, was inaccurate. So, we used a product feature to change our position and our value to them. In this case, we created tools that automated the clean up of their data for them. The catch? The data resided in our data center, so we had a better, cleaner, more accurate version of their data than they did. That not only attracted them to using our product in the first place, it raised switching costs if anyone else entered with a competitive product.
- Be aware of target market and user changes. Be aware of subtle or even dramatic shifts in how your software may be used or who may be using it when creating or adding an analytics solution. Your software, service, or product may provide a functional purpose at one level or position, but the analytics may be used at another level or they may be attractive for a target customer that you didn't reach previously. At PublicEngines we discovered through customer validation that our predictive analytics solution would more likely be used at a very tactical level, much different than our previous products. That had several implications. First, it made sense that we approached the market with a separate and distinct product. It also had implications for the UI, who the decision maker was for purchasing the product, and how we sold the benefits of the analytics solution. Finally, it opened up our solution to target customers that we didn't reach previously because it automated roles that they previously couldn't afford to hire.
- Focus on user experience over function. When I think about user experience, I think about who, how, and what they will get out of the solution. Sometimes we get caught up in what "solution" or how much of the stack we can provide rather than what the user needs to get out of it. One example is that there is a tendency to focus on a higher level order of analytics, with predictive analytics being at the pinnacle, rather than what the user needs. Not all problems are solved by predictions. Analytics comes in many flavors: visualizations, descriptive, normative, operational, and predictive, just to name a few. Make sure you are providing analytics that are usable. Another example where products often fail in experience is in the user interface. This is especially true with agile-based development teams, which invite a level of incrementalism that piles on new features and operations until the user experience becomes cluttered. Make sure you step back and take a functional design approach that aims to provide the right information in the right way. Square describes their purchase analytics as "Data made Digestible." That's a good standard to improve any user experience.
- Create a path to user success. To ensure your own success, make sure that you understand how to make your customer successful. Too often developers feel like they need to expose every analytics visualization, summary, and comparison in a buffet of graphs and data. This approach shows a lack of understanding of what the customer needs to be successful. I see this even in consumer products like fitness trackers (FitBit). Ground your product's success in research and validation by finding out what people really need and focusing on it. The feedback can be invaluable. In one product, our customer validation led us to adding historical, descriptive information embedded into our predictions that ended up being one of the most significant differentiating features because it helped users know how to use the predictions. In another recent product I worked on, we used customer feedback to reduce the analytics information down to three key metrics that we knew were the basis of customer success and greatly simplified our user interface to match it.
- Align your economic model. Finally, evaluate how to align your economic (pricing) model with the value that you provide. Most companies want to immediately charge for analytics as a separate product or module. It has worked for me in several companies I worked with because analytics provided such a significant value to a distinct target user. But it's not always the case. Square, for example, provides analytics as an additional set of features that comes with the product, and the benefit to Square is to differentiate themselves from the competition. Other opportunities may also present themselves in charging other parties for data analytics or insight, especially when you can aggregate data and provide normative analytics or by selectively giving some pieces away for free and charging for others. You may also find analytics providing value in other ways. Reed Hastings, CEO of Netflix, for example, recently revealed that his company spends $150 million per year on their analytics-based recommendation engine. It is not a feature that users would pay for, but NetFlix saves money by increasing customer satisfaction and reducing churn. I am sure they also see benefits in using their analytics engine to determine the value of new content, likely saving them millions of dollars per year.
What have you seen that has helped organizations be more successful at offering analytics solutions?