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 in fact a great opportunity -- if only the industry could unlock the potential and hidden value of data.
Given that many of data are driven by customers, businesses are positively teeming with customer data available from an increasing number of sources. In the ultra-competitive battle for customers, it's a race to capture as many data and information about customers as possible.
Great, you have the data. Now what? Taken as discrete pieces, data points are essentially meaningless. Data are worthless without the ability to translate the data into actionable insights and intelligence, which is where customer analytics plays a vital part.
Customer analytics is a customer-centric approach to guiding, driving, managing, and optimizing key customer decisions, based on a systematic analysis of the data available on customers and their behavior -- which can translate into a veritable goldmine for targeted marketing campaigns.
Most companies are in fact already using some form of customer analytics. To gain a better competitive advantage, businesses are hungry for more sophisticated analytics, to drive the biggest impact from the data and achieve true advantages.
The good news is there are a number of advanced statistical tools and data analysis techniques that enable more in-depth customer analytics and actionable customer intelligence, including predictive analytics (PA), customer lifetime value (CLV) analysis, and mixed market modeling (MMM).
Stop crunching your forehead, and let's take a look at what these tools can do for you.
Predictive analytics (PA)
Predictive analytics is primarily used by companies with a strong customer focus, such as retail, financial, communication, and marketing. A form of statistical analysis, PA uncovers relationships between explanatory variables and the predicted variables from past occurrences and patterns in data. That data are then used to predict future trends of customer behaviors, which is reliable enough to shape decision-making, forecast trends, and ultimately to identify and reduce potential risks and improve performance.
Predictive analytics is about sifting through the noise and the patterns in data, deriving actionable information and insights to enable and shape more proactive decision making, and ultimately improve business performance; it helps connect data to effective action. The crux is you need enough quality data to accurately identify and extrapolate patterns and predict future behaviors.
The more a business can predict the future, the better it can make intelligent business decisions. If you can predict short- and long-term behavior, you can affect short- and long-term behavior as well.
Customer lifetime value (CLV)
In marketing, customer lifetime value (CLV) is simply a unified measure of the value of a customer in each segment (i.e. 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-terms investments, CLV is a critical metric for any business that is customer centric. At a micro level, CLV helps companies decide which tactics should be used for which customer segment, recognizing 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 very difficult to capture and quantify a consumer's social influence. Consequently, CLV will ultimately need to include some measure of social influence whether positive or negative.
Those businesses that are able to measure and maximize the lifetime value of their customers while minimizing their investments have a distinct competitive advantage over those who do not.
Marketing mix modeling (MMM)
Most senior executives are being challenged with improving performance (revenue, sales, and profits) within budget constraints, while facing an increasingly fragmented media landscape.
This increases demands for brand and marketing managers to identify and optimize the combination of marketing and advertising investments that are the most likely to increase sales and improve overall marketing ROI. Given this increased pressure, companies have turned to marketing mix modeling (MMM) and other complex statistical modeling methods.
MMM is an advanced statistical analysis technique that 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. Marketing mix modeling (MMM) can be successful only if relevant, specific, accurate, and clean data are available upon which the modeling can be based.
However, marketers must accept a couple things:
- The data will never be perfect or complete. There will always be missing or imperfect data and, therefore, there should be a point at which the data are good enough.
- The model will not be able to explain or predict 100 percent of all marketing and sales activities.
Marketers across all industries, including CPG/FMCG, financial services, telecommunications, retailers, entertainment, pharmaceutical, and more have adapted MMM into their planning processes 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 analyze 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 realizing the potential of data -- at every point of the customer life cycle.
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