Tools that are turning data into action

Right now, we're in the midst of a data explosion, thanks to advancements in technology and, of course, the internet.

This data deluge is a great opportunity if only the industry unlocks the potential and hidden value of data.

Businesses are teeming with customer data available from an increasing number of sources. So what? Taken as discrete pieces, data points are essentially meaningless without the ability to translate it into actionable insights and intelligence. That’s why customer analytics is so important.

Customer analytics is a customer-centric approach to guiding, driving, managing, and optimising key customer decisions, based on a systematic analysis of the data available on customers and their behaviour. It can translate into a veritable goldmine for targeted marketing campaigns.

There are a number of advanced statistical tools and data analysis techniques that enable in-depth customer analytics and actionable customer intelligence, including predictive analytics (PA), customer lifetime value (CLV) analysis, and mixed market modelling (MMM).

Stop crunching your forehead, and let's take a look at what these tools can do for you.

Predictive analytics

This is primarily used by companies with a strong consumer focus, such as retail and finance.

A form of statistical analysis, PA uncovers relationships between explanatory variables and the predicted variables from past occurrences and patterns. The data is then used to predict future trends of customer behaviours, which is reliable enough to shape decision-making, forecast trends, reduce potential risks and improve business performance.

The key is to have enough quality data to accurately identify and extrapolate patterns. The more a business can predict the future, the better it can make intelligent business decisions. If you can predict short- and long-term behaviour, you can affect short- and long-term behaviour as well.

Customer lifetime value

In marketing, customer lifetime value is the amount of net revenue or profit from a given customer over their entire lifetime engaging with the business, product, or company.

Essentially treating customers as long-term investments, CLV is a critical metric for any customer centric business.

At a micro level, CLV helps companies decide which tactics should be used for each customer segment, recognising that all customers are different in terms of revenue per customer, cost per acquisition, and other metrics. At a macro level, it is the key ingredient in calculating customer equity; the total combined lifetime value of all the company's customers.

However, one of the inherent limitations of CLV is that you simply cannot quantify every aspect of value. For example, with the ever-increasing relevance of social media, it's difficult (but necessary) to capture and quantify a consumer's social influence, whether positive or negative.

Those businesses that are able to measure and maximise the lifetime value of their customers whilst minimising their investments have a distinct competitive advantage over those who do not.

Marketing mix modelling

Most senior executives are being challenged with improving performance (revenue, sales, and profits) within budget constraints, while facing an increasingly fragmented media landscape.  

Given this increased pressure, companies have turned to marketing mix modelling (MMM) and other complex statistical modelling methods.

This advanced statistical analysis technique links multiple internal and external independent marketing variables or inputs – advertising, media delivery and weight levels, promotion, pricing, sales activities, and other significant influencers like competition and market conditions – and explains how these contribute to changes in marketing and sales outcomes.

Once the model is built and validated, the input variables can be manipulated to determine the net effect on a company's revenue, sales, or profits outcomes. MMM can be successful when based on relevant, specific, accurate and clean data.

However, marketers must accept a couple things:

· The data will never be perfect or complete, so the model will not be able to explain or predict 100 percent of all marketing and sales activities. It is useful, though, to gain forward-looking recommendations on how to adjust marketing strategies, plan and allocate budgets, manage channels, create pricing strategies, and produce the highest marketing ROI.

· Marketing success begins and ends with the customer. For businesses to understand their customers on a deeper level, marketers must aggregate and analyse all of their disparate data assets. An integrated customer analytics and data-driven decision management solution, performed in a structured and comprehensive analytics environment, is the key to releasing the power and realising the potential of data – at every point of the customer life cycle.

Carolyn Bollaci (pictured) is regional VP ANZ at DG Mediaminds and George Musi is head of cross-media analytics at DG Mediaminds in the US.