7 Digital Analytics Trends that will Dominate in 2017 – 2018

7 digital analytics trends that will dominate in 2016

Analytics without application to an actionable strategy is meaningless”- Mike Grigsby.

In the preliminary days of web analytics, things were quite simple for marketers. There were fewer marketing channels and primarily one device to look out for i.e. the desktop. But today, with the proliferation of channels and devices it makes it extremely daunting for marketers and analysts to comprehend and measure customer journey.

2016 – 2017 has mostly revolved around predictive analytics, big data, emerging platforms, system integration, and new tools.  As per Newton’s theory, a change in motion of a body indicates a force in operation and this is the time for this force from 2015 to drive the momentum of marketing analytics in 2016.


Here are 7 Digital Analytics Trends I believe will dominate in 2017

1. Prescriptive Analytics

2016 was a shift from descriptive analytics to predictive analytics, but 2017 would be about upgrading from predictive to prescriptive analytics. Marketers not only want to know about what will happen next but also want to know what the optimal action should be. With automation being the theme everywhere, the next level is about data (insights) driving decision automation.

Descriptive analytics to a certain extent is automated and in 2017 – 2018 you’d see more investment in driving insights and automated decision making instead of mere reporting of data. Companies are moving towards machine learning and to make it more accessible we have services from Google Prediction API, IBM’s Watson Analytics, Amazon Machine Learning and Azure ML Studio from Microsoft. Analytics would be used to understand customer patterns and programmatically suggest profitable customer paths to marketers to route customers in that direction. For instance, Amazon uses prescriptive analytics for product recommendations based on customer data around original purchase and product engagement patterns. This helps Amazon to provide better user experience and also increases customer spend.


2. Customer’s Perspective in Defining Metrics

Until recently most analysts have been creating a measurement framework based on a funnel based approach where the focus is on the acquisition, volume-based metrics and not on customer profitability.  McKinsey’s analysis of a recent ANA survey revealed that “only 13 percent of companies feel strongly that they have identified their customers’ decision journeys and understand where to focus marketing, while nearly half cannot measure the critical stages of the consumer decision journey”.


Companies have been obsessed about channel metrics and this is the time when the need is to shift focus to a more customer-centric measurement framework. 2017 would be about aligning customer journey perspective with the firm’s perspective of conversion funnel i.e. which analytics method to use to measure business objective at each stage of the consumer purchase cycle.  This would eventually drive the company strategy to be centered on consumer behavior. You can read more here.


3. Monetization of Data

In the current digital landscape, companies are sitting on massive volumes of data (raw & processed). Now is the time for businesses to look at innovative ways of leveraging the data, for driving future action and direct impact to bottom-line revenues, which is now readily available.

Data around customer transactions, interactions across devices and channels needs to be assessed based on their value and the best possible strategy needs to be developed to utilize full potential of data monetization, either in the form of increasing revenue streams or using the data to create efficacies within the organization to reduce cost. Successful digital revolution will be based on forming data streams in and out of the function and finding new ways to monetize them. One of the early adopters of this concept in the e-commerce industry is Amazon and they are already leveraging their efforts in this direction.


4. Multi-channel Attribution

With the growing number of devices and platforms, it is the context which decides which device and platform a consumer would use. Based on the amount of time a person has, the task they want to achieve, their location and most importantly their state of mind will decide a preferred mode.

As per Google’s report in 2012, 67% of online shoppers had used multiple devices sequentially to shop online. Now imagine how big this number would be in 2017! The key trend for analysts in 2017 would be to decipher an attribution model for the multiple channels a consumer is exposed to. For instance, during the initial phases of customer purchase journey i.e. need recognition and information search, social media plays an important role and is accessed mostly on mobiles phones. Whereas the actual purchase may happen on the desktop site. Now this should not undermine the importance of social media on mobile phones. Hence the key thing is the attribution of success to each of the channel-platform combination as per the relevant context


5. Focus on ‘Return on Analytics Investment

The fight over getting adequate attention and resources for analytics is long over. Now companies have realized the importance of it and more and more organizations prefer to build an in-house analytics team. But with great power comes great responsibilities and organizations would have to understand the ROI of analytics i.e. the Return on Analytics Investments. Though it is the analytics team which measures the success of all other teams, but they now need to do the same for themselves, it’s a leap forward from being a cost center to a profit center.

One of the recent articles from Harvard Business Review on ‘Quantifying the Impact of Marketing Analytics’ states that “companies currently spend 6.7% of their marketing budgets on analytics and expect to spend 11.1% over the next three years. Also, more than a billion dollars have been invested in data analytics companies this year”. Given all the money being spent on analytics, there is surprisingly little scrutiny about their impact. Hence, return on analytics investment is going to be the focus in coming years as companies would want to know if they are using marketing analytics effectively or not. You can read more about it on HBR.


Editor’s Note : To deepen your understanding of Data Analytics and grow in your career, you should enroll for Coursera’s course on Marketing Analytics and Digital Analytics. You can also look at multiple other courses on Analytics here.

6. Empowerment

For reports and dashboards, businesses have started moving towards automated self-serve solutions which will give them a view of their KPI’s. 2017 would be about enabling these decision makers with easy to access processed information and enabling technologists with ease of managing means to this information.


7. Analytics Governance

With massive investments in analytics space it’s extremely critical to ensure organizations focus on a robust governance mechanism and data integrity, as the number of stakeholders accessing analytics data and tools inside an organization is increasing every year. This is leading to a state where everyone is looking at diverse data sets and taking decisions based on common metrics with their own understanding of the data. Having an “Analytics Center of Excellence” to ensure all the teams have access to one version of data and its interpretation, are speaking the same language and looking at the same data should be a priority this year. With such a dynamic environment where data is fast changing the landscape of competition, companies now need to shift to a data driven culture to take advantage of tools, people and processes they have implemented which can be managed with analytics governance.


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