Do you want to leverage business value with IT?

This bundle contains a full perspective spelled out in a storyboard format and is the perfect toolkit for the experienced or novice CIO, CTO, IT Executive, Enterprise Architect.

The blue prints have and can be used to transform the IT Value proposition and have been used across the globe.

WHAT BUYERS HAVE SAID: “I would like to take the opportunity to thank you for all your inspiring ideas, sources of information and thoughts. Makes much value for me – as I feel confident using it as references and sources of inspiration and validation.” Experienced Enterprise Architect with 10+ years experience in a Big 5 context

WHAT THE REVIEWERS SAID: “ The “Deliver Business Value with IT” series is an extremely solid piece of work that comes across as the A-Z reference of how to execute and implement IT strategy from a CIO level perspective. The reader will learn robust approaches to deliver services designed to support IT and Business drivers. The perspective that Martin spells out permits an overview of how to leverage existing frameworks but also to effectively support the execution of an IT Strategy aligned with the Business Strategy.”

“The focus that Martin takes in the “Deliver Business Value with IT” series will help in tackling the seven main non-technical challenges any CIO or other senior IT business leaders will face:

  1. How and what should I communicate to whom in what way?
  2. What to think of when it comes to competences needed to provide my IT services?
  3. How to provide the best value at the best cost?
  4. What to think of when ensuring efficient and effective delivery of projects?
  5. How to establish a sourcing strategy and determining how to manage your vendors?
  6. What are the best practices for managing my operations, and what to think of?
  7. How can I best scan for and analyze emerging technologies?

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Do you want to leverage business value with IT?

Big Data, focus on the needs of the business

This excellent article from BCG offers a hands on approach on how to leverage Big Data for retail:

Traditional retailers generate and capture a deluge of data—most notably, customer transaction histories that can reveal detailed product affinities and promotional and marketing response rates. Now the emergence of big data and advanced analytical tools and techniques can connect data with a larger context. Big data can explain the who, what, when, where, why, and how of retailing.

Although some leading companies have gained a reputation for deft data handling, most retailers have not yet built the analytical capabilities and internal processes necessary to take advantage of the deep well of information they can access. Merchants and marketers often rely on tactics that worked last year, with only slight modifications. Sometimes their promotions end up discounting the wrong items and hurting rather than helping sales. Too frequently, they simply rely on consumer goods companies or suppliers, with their different incentives and motivations, to tell them what to do.

In the end, many retailers have not figured out where and why they are winning and where and why they are losing. They struggle to discover which prices, promotions, and store locations are working best. They have a hard time taking advantage of all the contextual information around transactions that could make a difference in sales. In effect, they know the outcomes of millions of real-time experiments, but they are not able to look at and learn from them.

All this leads to missed opportunities. Ultimately, it opens doors to online and direct sellers, which often have better data and more sophisticated analytics.

Three High-Potential Opportunities

Big data can help turn this situation around. But in such a fluid technology landscape, it can be hard for retailers to tell where to focus their big-data efforts: which projects are a flash in the pan, and which ones will create growth opportunities and competitive advantage?

In our work with retailers across a range of market segments, we see three opportunities that offer high potential in the near term. Exploiting them can generate a significant increase in revenues and profits for retailers.

Boosting the Effectiveness of Promotions. Promotions are a common source of success and frustration for retailers. Overall, promotions tend to have a dramatic effect on retail sales. But once retailers start discounting, they are often hesitant to reduce promotional activity, because of the steady pressure to increase top-line growth. Making matters worse, most retailers do not have a handle on how their promotions actually perform.

Finally, many retailers rely heavily on suppliers for the selection of promotions. However, vendors often confuse retailers by providing conflicting consumer research in support of their brands. And they tend not to care if a promotion boosts sales for their product line while having the unintended consequence of lowering sales in the overall product category.

As a result, 30 to 50 percent of promotions have no positive impact on sales and margins. Even worse, many of them reduce profits without leading to additional sales.

Understanding the performance of promotions is difficult, because the necessary analysis is complex and because processes are interdependent. As a first step, retailers need to establish an accurate baseline of sales without promotions to understand the direct lift from a promotion and the cost of the discount. Retailers also need to tease out the secondary effects of promotions. Cannibalization can result when the promoted item reduces sales of other, substitutable items. A “halo effect” can result from the sale of additional products when shoppers come in for the discounted product. And the “pull forward effect” can depress item and category sales following a promotion, since consumers have already stocked up on the products.

In addition, retailers must determine how much support vendors are really providing for promotions. That includes finding out how much of the funding that manufacturers provide is in fact associated with promotional activity. Retailers also need to capture the operational implications of promotions, including incremental marketing, supply-chain, and store-labor costs.

Doing this kind of analysis on a single promotion requires good data and techniques but is not all that complicated for large retailers. However, executing this analysis across the thousands of item level promotions running concurrently and isolating external effects such as season and weather can be extremely complex, even for sophisticated retailers. It can be hard enough to determine what happened as a result of a promotion, let alone what might have happened if the company did not run the promotion. Even more challenging is to translate this analysis into tangible actions that buyers can take to improve performance.

In short, the analysis requires a huge amount of varied and detailed big data and advanced analytical techniques to sort out the interdependencies. But the payoff is worth the effort. We consistently see potential improvements in profit margins of around 1 percent of sales, depending on a retailer’s starting position and the share of income it derives from items on promotion. That can represent 20 percent of a retailer’s entire net income, which can be invested in lower prices, better store layouts, and improved shareholder returns.

One grocery retailer achieved these kinds of results after seeing poor performance from promotions. As a result of the kinds of advanced analytical techniques described above, the grocer was able to tailor promotions to products, with some brands receiving additional promotions, others a price cut, and some no promotion.

Targeting Pricing Precisely. Retailers often struggle to set the right prices while trying to provide a consistent pricing experience to customers across stores. We find that pricing zones, or areas with similar pricing, are typically determined on the basis of how a retailer organizes its activities geographically. Setting prices in this way often causes retailers to place a great deal of emphasis on city, state, and country borders. When people cross these sometimes arbitrary boundaries to shop, however, they see different prices from the same retailer.

As a result, customers might cherry-pick the best prices from nearby locations. Also, less price-sensitive customers might pay less than they may have been willing to, and more price-sensitive customers might not buy at all. Frequently, retailers create too few pricing zones, which limits their ability to respond in a more nuanced way to competitive pressures.

A better approach is to use individual customer behavior as the foundation for establishing pricing zones. Rich transactional data from loyalty programs and credit cards across thousands of stores can be used to understand where individual customers tend to shop. That data can be layered onto an analysis of current store locations and pricing zones. All this information can be combined, analyzed, and mapped to show geographic clusters of price awareness based on observed shopping behavior rather than artificially set boundaries.

This is the kind of complex, messy information from which big-data projects excel at generating insight. As a result of an advanced analytical approach, retailers can determine the likelihood of an individual customer shopping at different stores. They can then justify charging different prices across a region, be confident that pricing performance will not erode, and ensure that customers will not be likely to encounter noticeably different prices within the area where they usually shop.

Consider the case of a national retailer that used state boundaries to establish one set of prices across most of the Greater Atlanta area for a group of products essential to drawing customers to stores. An analysis of loyalty card data revealed considerable complexity in customers’ shopping patterns across Georgia and adjacent states. Even within Atlanta, the retailer discovered a wide diversity of customer demographics and shopping behaviors. For instance, because of Atlanta’s sprawl and difficult commutes, stores serving the same cluster of customers tended to be located along commuting corridors.

By examining where customers shopped, the retailer identified about a dozen unique store clusters in Georgia alone. (See Exhibit 2.) The analysis gave the retailer a strong rationale to establish as much as a 30 percent price difference for the set of products across store clusters, within a region that previously had had only one set of prices. The retailer raised prices in some store clusters while still offering the lowest available price in the area. At the same time, it lowered prices in other store clusters just enough to beat the local competition. Customers throughout the region saw competitive prices in their shopping area, while the retailer generated $5 million in additional profits within a single category from the combination of higher prices and additional store visits. Through a big-data analysis of billions of transactions, the retailer was able to scale the approach to the entire U.S., resulting in a much more refined approach to pricing, without causing customers to notice the multiple price points.

Understanding the Value of a Network. Retailers often view the value of their network of stores—the “last mile” of retail—in terms of the value of the land and the buildings. A network of brick-and-mortar stores is seen as a fixed cost tethered to a physical location. Because of the long-term costs of site location, real estate, and construction, changing a network of hundreds of stores can be time consuming and expensive.

These days, executives often look at a network as a burden, rather than an opportunity. That burden is made even heavier as sales migrate online and leave many brick-and-mortar stores with too much space or an insufficient network to meet demand.

Big data reveals another way to think about store networks. One specialty retailer was able to unlock value from its retail footprint with an advanced analysis of its network. Consumer research suggested that a well-known technology brand had not achieved its full potential for sales among a key customer segment, in part because its existing direct-sales network was spotty in many areas. The retailer believed it could help the brand provide the necessary coverage for those businesses, but it needed proof.

The retailer decided to analyze how well 12 million businesses and 117 million households in the United States were covered by its network, by its competitors’
networks, and by additional retailers that could be partners with the technology brand. It used geoanalytical techniques to examine 3.4 billion point-to-point customer trips in order to identify gaps in the technology brand’s retail and partner network.

The results were clear: The specialty retailer was 37 percent closer on average to U.S. households and businesses than its competitors were. In fact, the retailer had the best coverage of key customer segments when compared with competitors and other potential partners in almost all U.S. regions. The company determined the true value of its last mile by looking at it through the lens of a complementary player.

As a result, the retailer was able to partner with the technology brand to generate $40 million in incremental revenues from its existing network at little additional cost. It also gained a new understanding of the value of its footprint. We believe that other retailers can achieve results from their network on the same relative order of magnitude by using a wide variety of tactics, aided greatly by big data and geoanalytics.

How to Begin

As retailers explore these opportunities aided by big data, they should take the following initial steps:

  • Focus on the most pressing opportunities. Retail executives are highly pragmatic: companies win by improving sales or margins every day. Executives should determine how to fuel growth in specific ways. They should not try to build a “complete solution.”
  • Start with the data you truly need. Executives should certainly solidify their infrastructure. Sales, costs, promotions, space, store locations, and customer data should be connected, but only insofar as that will drive value for the business. Companies will likely have to connect more data as efforts evolve. To achieve results quickly, they should begin with just the data that is required in the near term.
  • Bring the organization along. The people who make daily decisions in retail—buyers, trade planners, and others—are hungry for useful information. Help them trust the output of the analysis by including them in the process and being transparent about it. This will improve your results and make them much more relevant.
  • Translate the analysis into tangible actions for the broader organization to validate.Ultimately, big data in retailing needs to help people make practical decisions faster and easier: Should we promote a product for an extra week? Should we offer a two-for-one deal? Which promotions should we continue and which should we stop? Make sure that recommendations resonate with those making the daily decisions. If they can’t respond to new information easily, they will ignore it.
  • Maintain trust. One-to-one marketing and individualized pricing can sometimes be taken so far that customers lose trust in a company’s basic fairness in pricing or its ability to protect their privacy or data. To create trust and gain access to even greater amounts of personal data for big-data applications, retailers must communicate transparently how they use the data and must demonstrate the important benefits to consumers from the new analytical techniques.

Big Data, focus on the needs of the business

Mobile first, key findings from South Korea

Based on research performed on the South Korean market McKinsey & Company present the follow findings:


Mobile-channel buyers have distinct demographics. In South Korea, women account for 60 percent of transactions. Additionally, most are in their 30s and are likely to have preschool-age kids. They are also, somewhat surprisingly, likely to be full-time housewives. There has been an assumption that m-commerce is dominated by busy working moms; in fact, working moms spend much more time in front of a PC, mostly at their jobs, while housewives or moms with young kids are more likely to use their smartphones to shop. Companies have noticed: social-commerce player Coupang has aggressively targeted mobile-savvy young moms by offering baby gear such as diapers at low prices.

Yet mobile shoppers, regardless of their age, gender, or life circumstances, seem united in turning away from stores and online retailing. Our research found that among those who shopped on a mobile device, 13 percent did not shop in stores, and 53 percent did not shop online. Increasingly, these consumers can only be reached through their smartphones: We found that offline and in-store marketing motivates only 7 percent and 2 percent of mobile purchases, respectively. Yet mobile ads or promotions influence three out of four mobile purchases.


Since it’s harder to compare products and study details on a phone’s small screen, mobile shoppers deliberate less when making purchasing decisions. Our research shows that more than half of mobile consumer decision journeys—from considering products to purchasing—last just a single day, compared with only 36 percent online. In addition, mobile shoppers visit on average fewer than two sites before making a purchase, versus 2.75 for online shoppers. In essence, m-commerce consumers are driven much more by impulse than by product features or prices: some 17 percent of mobile transactions in South Korea are made without prior research, compared with just 6 percent of online transactions.

For retailers, this has enormous implications. While it has been critical for online retailers to keep a long tail of products in order to capture whatever consumers are searching for, mobile shoppers want quick satisfaction. Their purchasing decisions are often governed by impulsive or emotional factors (which encompass product categories including apparel, fashion accessories, and shoes) or habit (such as buying groceries and kid/baby items). For its mobile dedicated shopping platform, for example, online market 11th Street has reduced its total number of SKUs to only 7,000 and emphasizes “deals of the day.”


In contrast to the bargain-hunting mentality that pervades online, mobile shoppers place the greatest value on intuitively easy navigation and convenient shopping experiences. In our research, more than 60 percent of South Korea’s mobile shoppers cited convenience as their top priority, compared with 44 percent of online shoppers. To connect with mobile buyers, many successful retailers are providing less information on their mobile sites. Quick delivery of products is also essential for many regular mobile shoppers, particularly those who buy groceries and other staples. To satisfy this consumer demand and expedite the delivery of products purchased on mobile, GS Shop opened a mobile-dedicated warehouse, for example, and many mobile-commerce players now offer next-day delivery for grocery and kid/baby items. GS Shop has opened a mobile-specific call center so shoppers can get specialized assistance with a single click, and players are also adopting new payment solutions such as KakaoPay, a social-media-based payment system.


Consumers are known to buy online from a smaller number of retailers than they use when they shop in brick-and-mortar stores. Similarly, mobile consumers are more likely to go directly to a retailer’s site or app than to use a search engine, meaning there is a significant opportunity for retailers to lock in customers. As a result, South Korea’s m-commerce players use multiple tactics to drive repeat visits, offering mileage points or coupons to those who interact at least once each day with their mobile site or application.

Connecting the mobile-shopping experience to physical stores also goes a long way toward building a true omnichannel experience and locking in customers. The mobile application for retailer Lotte, for example, offers an “in-store mode,” where shoppers get real-time mobile notification of promotions and coupons when they step into one of its department stores and pass a specific brand’s area. Hypermarket Emart has a virtual-store application that shows products displayed in the same layout as in its physical stores to provide an easy, consistent shopping experience.

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Mobile first, key findings from South Korea