According to McKinsey, a comfortable 20% of marketing budgets can actually be redirected to other necessary expenditure without the risk of losing marketing ROI, which amounts to about $200 billion of global marketing budget. This view has been further supported by many worldwide analysts.
In the present data economy, where is enterprise marketing headed with big data? What may be considered a necessary marketing expenditure?
Our research attests that big-data technology enabled marketing teams are becoming an unchallenged value addition in any enterprise. Increasingly marketers need to extend the traditional marketing mix to a more data-technology oriented marketing strategy. Big data has proven its worth to enterprise marketing.
While research-data sample size indicates the technology penetration has not happened as widely as forecasted, today’s marketing departments have seriously started reconsidering redistributing their budgets to include smart analytics at every facet of marketing. Two recent trends in technology have reinforced the technology enabled marketing mix concept more convincingly: the availability of high-quality data, and the emergence of powerful analysis tools.
What big data means to marketers
Industry sectors, large or small, have all tasted the power of big data in varying degrees. While most industry leaders understand that the benefits of this catch-all technology will not be realized overnight, they recognize the urgent need to move forward from the past practice of simply exploring probable definitions of this complex term.
Fatemeh Khatibloo, a senior Forrester Analyst, feels this is the right time to explore big-data technologies as many vendors are aggressively pushing their analytics platforms which claim to magically solve any and every marketing problem.
As Ms. Khatibloo rightly points out, big-data is not simply a conglomeration of technologies, tools, high-velocity data, and high-volume data. Big data, according to her is a journey that every enterprise must undertake to to close the gap between the data that’s available to them, and the business insights they’re deriving from that data. During her research with her colleague on the visible opportunities and challenges of big data, she discovered several important issues, such as:
- The immense potential of business data to disrupt the existing models of business for positive transformation.
- The risks and unknown threats associated with the application of this technology, such as privacy issues, legal violations, and ethical concerns arising from possible misuse of classified data.
- From the last five years of defining, now it’s time to move onto the hows and whys of big data, which includes changing the organizational culture, nurturing the right talent, and gradually building the big-data infrastructure to transform business results.
- Marketing departments have to serve as advisors to their technology peers, and step up activities to bridge this yawning gap between marketing goals and objectives and available technological solutions.
Jonathan Gordon, Jesko Perrey, and Dennis Spillecke, Partners at McKinsey & Company, and the co-authors of Big Data, Analytics, and the Future of Marketing & Sales, feel that big-data is possibly the biggest game-changer for marketing and sales ever since the Internet penetrated the mainstream society about 20 years ago.
In their e-book, these authors attempt to explain how the recent data explosion and emerging data technologies together present rare opportunities to study customer behavior. While most organizations are inundated with vast amounts of data on one handthe changing customer behavior on the other handprovides unique challenges to compete in the current competitive market. The highest impact of this present wave has been felt in the rapidly surfacing, digital marketing channels and platforms.
According to this McKinsey research report, companies that aggressively use data-technology driven marketing and sales decisions stand a chance to improve their marketing return on investment (MROI) by 15 -20 percent.
It may be of interest to note that this 15-20 percent MROI translates to about $150-$200 billion of value-add, considering the global annual marketing spend of an estimated $1 trillion.
McKinsey’s Tim McGuire on three key challenges of making data analytics work
Transforming marketing through smart analytics
The popular belief in the industry is that data, by itself, cannot transform business unless it is aided by decision-making, analytical tools. This argument holds true for marketing analytics as well. Marketing data, however high-volume and high-velocity, will deliver no value unless that data have been processed by insightful analytical tools.
- Data by itself cannot transform business marketing: The hidden power of big data lies in its ability to discover hidden trends and patterns in disparate data that could lead to some competitive advantage in business. Data discovery can also unlock potential markets, potential customer segments, or solve complex problems.
In most cases, this data discovery process requires exploring data in new ways. For example, a specific micro-market analysis in a chemical company revealed that while this company enjoyed a 20 percent market share of the overall market, it ruled up to 60 percent of some niche markets, and 10 percent of some othersdemonstrating the capability of big-data analytics to uncover micro segments.
- Pre-purchase customer behavior proves to be a goldmine: Studying and analyzing the development of customer decisions prior to a particular purchase, can unravel potential customer winning strategies, or can protect existing customers from the prying eyes of competition. More than a third of all pre-purchase activities today are online, which indicates the power of effective digital marketing strategies. These strategies may include tailored marketing campaigns based on customer data analytics, highly targeted product messages and offers, and personalized customer engagement through emails, landing pages, and social media.
- Need for automation to handle data deluge: As data volume continues to grow exponentially, the need for automating data acquisition and processing is becoming imperative. Algorithmic marketing, which combines machine learning, predictive analytics, and natural language processingmay soon become common practice to tackle vast loads of marketing data in enterprises. These kinds of automated tools are usually designed to act on triggers like customer search preferences, price comparisons, or inventory.
Big data analytics can also be used to empower front-line operations like customer-service or sales representatives. The success of this application will lie solely on simplifying the actionable decisions for the front-line staff. As an example of this practice, you can consider the case of a cargo airline that developed a complex algorithm to study the frequently changing dynamics of the cargo industry, leading to a supply-demand based negotiating strategy. But what the system delivered to its front-line sales staff was a friendly dashboard that revealed flight capacity, corresponding pricing, and competitor options. This automated system boosted sales by 20 percent.
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