How do you measure the ROI of analytics? As with all ROI calculations, we need to understand what the investment levels are and then what the returns are.
It may seem like a strange question but it is one that still tends to come up. Last week on a discussion thread, for example, someone was asking about how to demonstrate the return on investment (ROI) in analytics. That's a nice irony, I thought, considering analytics spends most of its time measuring the ROI of marketing investment. Isn't that ROI enough in itself? Apparently not, if the question is still being asked. So how do you measure the ROI of analytics?
As with all ROI calculations, we need to understand what the investment levels are and then what the returns are. In the case of analytics the investments are probably easier to work out, but it's necessary to calculate the "total cost of ownership" of the analytics capability of the business. These typically break down into three categories:
Technology costs are fairly self-evident and well understood. Most digital analytics technologies operate on some kind of subscription model, but other pricing models include licence costs with maintenance or renewal fees in subsequent years. The key aspect for understanding ROI is not just to look at the annual or first-year costs but to look at them over a period of time, perhaps the expected average over the next three years.
Most analytical technologies require some level of services around them, either from the technology vendor or from other service providers such as consultancies. The U.S. and the European markets have all seen recent consolidation of consulting businesses into larger entities, perhaps with the associated impact on prices, but there remain sufficient smaller, niche, or boutique players to provide a competitive market.
I think it's important to allocate sufficient investment in services/consulting around an analytics technology, particularly in the early days of its adoption. They are not necessarily the simplest of technologies and there may be a learning curve involved. It's false economy I believe to make the (often significant) investment in a technology and then not to invest in having it configured properly for your business or having your people skilled up on it properly. I've seen this a number of times over the years and there's no hope of realising a decent ROI from the technology if companies under-invest at this critical early stage. The same principle also applies to the next category of cost: people.
In my opinion, people are the most important investment that an organization can make in developing its analytics capability. There can be trade-offs between developing an in-house capability versus out-sourcing to an alternative provider, but at the end of the day it will be the quality of the people that will count. There is also a balance to be made between acquiring technologies and acquiring skills. Smart people can do a lot with relatively simple technologies or open-source systems but you need the smart people if you are buying in smart tools. Otherwise it's like buying a high-performance car and giving it to someone who's just passed their driving test. It's only going to be a matter of time before there's an accident.
So the investments in analytics can be assessed, but what about the returns? And importantly for any specific analytics investment, what is the marginal return for the marginal cost? I think assessing the returns can be quite a tough job and broadly they break down into two types: the direct benefits or returns and the indirect ones. I'll be looking into these in more detail next time.
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Neil Mason is SVP, Customer Engagement at iJento. He is responsible for providing iJento clients with the most valuable customer insights and business benefits from iJento's digital and multichannel customer intelligence solutions.
Neil has been at the forefront of marketing analytics for over 25 years. Prior to joining iJento, Neil was Consultancy Director at Foviance, the UK's leading user experience and analytics consultancy, heading up the user experience design, research, and digital analytics practices. For the last 12 years Neil has worked predominantly in digital channels both as a marketer and as a consultant, combining a strong blend of commercial and technical understanding in the application of consumer insight to help major brands improve digital marketing performance. During this time he also served as a Director of the Web Analytics Association (DAA) for two years and currently serves as a Director Emeritus of the DAA. Neil is also a frequent speaker at conferences and events.
Neil's expertise ranges from advanced analytical techniques such as segmentation, predictive analytics, and modelling through to quantitative and qualitative customer research. Neil has a BA in Engineering from Cambridge University and an MBA and a postgraduate diploma in business and economic forecasting.
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