Presenting recent books: “Smart data” by Björn Bloching, Lars Luck and Thomas Ramge: Step by step to smart data
Big data – the promise of huge profits for many. Yet companies often fail to develop a functional big data strategy in their day-to-day business because the flood of data is simply too huge to overcome. One approach? Smart data. Authors Bloching, Luck and Ramge have delved into a special area of smart data to show how to optimize marketing measures and sales approaches by responding in a smart way rather than in a big way.
Anything that can be digitized will be digitized, something that authors Bloching, Luck and Ramge demonstrate using the example of different companies who are considered smart data champions in their industry. However, not every company has to become another Google, Amazon or Facebook based on its data skills. Less is sometimes more. US statistician and blogger Nate Silver is a perfect example of this. His predictions on Barack Obama’s initial election and subsequent reelection made well-known experts who relied on much larger data volumes seem outdated. Silver’s recipe for success was to constantly question which correlations were actually relevant.
Taking advantage of experience, theory and machine learning
So how can we handle data in a “smart” way? The aim is planned and focused data analysis that reduces costs or facilitates new sales, both in existing and new business segments. To do so, experience-based knowledge, theoretical models, statistical analytical methods and the capabilities of machine learning are all combined. Smart data works on a highly hypothetical basis and generally uses smaller datasets with high levels of heterogeneity. These smart data projects are particularly results-oriented, yet at the same time save on resources. They cannot overburden the organization, either financially or in terms of staff.
The smart data cycle and its five steps
The authors recommend a smart data cycle to handle data intelligently. This consists of the following steps: 1. asking the right questions; 2. using the right data; 3. understanding the customer; 4. optimizing the USP; 5. sending the right messages at the right time.
Even though digitization is changing so many things, the basic tasks and challenges facing companies remain the same: securing and increasing market shares, opening up new sales, customer retention, etc. Step one of the smart data cycle is first and foremost about achieving clarity regarding existing operational problems. This includes the question of how data can help with finding (better) solutions for the problems identified. For example, by using transactional and market research data to formulate more appropriate targeted marketing messages.
The cycle’s second step involves using the right data. Three questions are key here: What data sources that are already available can help with a solution? What data are missing? How hard would it be to get these missing data? This step is complete if a) the key features that are needed for a better sales approach have been defined; b) a good understanding of which data are needed to derive measures for this approach has been established; and c) those involved know how to obtain the missing data without overburdening the company technologically, financially or in terms of staffing.
A comprehensive, systematic baseline appraisal of the customer segmentations used by the company is carried out at the start of the third step. This often shows that different departments (strategic marketing, product marketing, sales, etc.) use different segmentation methodologies. These must all be brought in line with one another to make a standard, company-wide understanding of the customer possible. Yet the authors also advocate a pragmatic approach with realistic goals.
The next step is to develop a USP and optimize what is on offer. This involves a range of realistic, attractive product offers that can be tested, implemented and continuously optimized using data.
The fifth and final step involves finding the right sales approach. This begins with analyzing the current touchpoints: How does the company reach customers? How does it interact and what impact does this have? Whom has it had little or no contact with up until this point? Once these questions have been answered, companies can start to think about new points of contact and their desired effects.
Added value through smart data
The added value of this step-by-step approach lies in the company achieving an intelligent form of segmentation, which in turn leads to marketing measures with a good cost-benefit ratio. To show how this works in practice, the authors use the example of companies that utilize smart data to offer their customers added value, to make more efficient use of advertising budgets and to create other competitive advantages. For example, technical filters and human expertise ensure highly appropriate product recommendations when it comes to curated shopping offers. American drugstore chain Walgreens discovered that their branches were only attracting customers within a specific radius, so dropped all advertising spending outside of these regions. Retail group Walmart uses differentiated weather and sale data round the clock and on a regional basis to optimize its business’s supply and price strategy.
Not in conflict: transparency and focus on results
The factors for success that make a company smart include maintaining a managerial staff that accepts and even welcomes mistakes as long as lessons can be learned, as well as a new understanding of leadership. This takes into account the fact that the success of products or services is increasingly more difficult to plan. Transparency in terms of results and a focus on results are not contradictory in smart systems. Instead, they are two sides of the same coin. After all, experiments generate evidence for better decisions, products or processes.
And the right employees are a crucial factor in taking customer data to the next level. However, companies do not necessarily need a lot of new employees with new skills in order to achieve this. It is sufficient to identify those in the company to start, promote, expand and manage the smart data cycle.
Yet one thing in particular must never be forgotten with all these projects: consumers who share their data expect IT security, transparency, proportionality (i.e. not blindly and manically collecting data) and added value. If customers are able to trust in the fact that exchanging data is a business of reciprocity, they will be more willing to share the right data.
Björn Bloching, Lars Luck, Thomas Ramge: Smart Data. Data strategies that customers actually want and companies actually use. Redline Verlag, Munich 2015, 24.99 euros
Prof. Dr. Björn Bloching is responsible for the international digital division at Roland Berger Strategy Consultants. Björn Bloching was an early researcher into predictive analysis and is an expert on data-based marketing, multi-channel strategies and digital business models.
Lars Luck heads the portfolio strategy division at the Metro Group. Prior to this, he was a partner at corporate consultancy agency Roland Berger Strategy Consultants. As a corporate consultant, he advised leading manufacturers and retail companies on digital transformation, multi-channel management and marketing and sales.
Thomas Ramge is a technology correspondent for business magazine brand eins and writes about IT issues and the future of marketing. He is also a contributing editor for The Economist. He has been honored with various journalism awards for his work, including the Herbert Quandt Media Prize and the German Business Book Award (Wirtschaftsbuchpreis).
Author: Editorial team Future. Customer.
Image: kwanchaift – Fotolia/Adobe Stock