PREFERRED CHANNEL: MOBILE

Generating Deep Insights from the Customer Genome How to derive the digital DNA of customers TRAVELS FOR WORK

BRAND LOYAL

SHOPS ON SATURDAYS SAT

Introduction In the past, businesses relied on market and customer segmentations to guide their sales and service strategies. But in today’s digital era, the practice of distilling millions of customers into sub-categories based on customer relationship management information is no longer sufficient.

Now as businesses go digital, they must also evolve their customer engagement and interaction strategies to attract and retain digital customers—ones who are deeply connected, highly informed and consistently on-the-go. The power has shifted irrevocably in favor of the customer in terms of what they buy, when they buy, through which channel and at what price. Instead of vying for one-time transactions in the traditional purchase funnel, businesses must set a new goal: to form a loop of continuous, relevant connections with customers before, during and after purchases. (See Figure 1.)

From the customers’ perspective, this non-stop experience presents many opportunities to interact with a particular brand. It also results in an effortless consuming experience as they go about their daily lives. From the businesses’ point of view, it offers new opportunities to satisfy the ever-increasing demands of their customer base, strengthen brand loyalty and ultimately boost sales.

Figure 1: A continuous customer engagement strategy is required to satisfy today’s digital customers.

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In order to deliver this effortless customer experience, however, businesses must take a concerted approach. At Accenture Technology Labs, we suggest the answer lies in creating digital customer genomes, which we describe as the detailed digital DNA of businesses’ customers and the next generation of market and customer segmentation. Each DNA is built through a combination of traditional and alternate data sources, along with derived customer data that is created through advanced analytical methods. This organic derived data includes distinctive markers that businesses can apply to create targeted approaches to high-value customers and prospects. (See Figure 2.)

With customer genomes, businesses can develop a deeper understanding of individual customer needs, preferences and lifestyles. They can also streamline and manage inventory, distributing products to regions where clusters of customer genomes reside. Best of all, businesses can use the derived data to convert insights into actions, developing and delivering contextualized and personalized information that suits a specific customer need. (See Figures 3 and 4.)

The digital customer genome focuses on using traditional CRM data (such as demographics, purchase history and loyalty programs), alternate data (from sources such as social media profiles and community-based data) and derived data (static and dynamic insight describing an individual obtained from a further analysis of the selected data sets) to create a digital DNA of what every business should know about each of their customers.

Figure 2: The customer genome uses existing data sources to derive additional information about customers in order to develop a genome code that is unique to an individual.

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Alternate Data

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THE CUSTOMER GENOME

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Figure 3: This hypothetical customer genome, depicted in a Sankey diagram, provides a complete view of a customer across all channels and systems. It combines traditional and alternate data sources (demographic, transactional, social media) into derived data, which businesses can use to anticipate future behavior and create stronger, longer-lasting relationships with customers.

The Economist Open Source Software

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These actionable insights are where the full value of the customer genome comes into play. Examples include inferring future product and service needs, or personalizing offers to individual customers as they shop online or via their mobile device. For instance, a business can use the derived data of a target customer to infer preferences in music, entertainment or social activities. Say a banking customer “likes” a particular book on his social media profile, in this case, “Hamlet.” When his bank learns a local San Jose theater is doing a production of the play, it can reference his genome and provide budget recommendations to help the customer save money for the tickets.

In this way, the bank is tailoring content and extending services to support the specific interests or needs of an individual customer. The result is an increase in customer loyalty. When businesses use a customer genome to the full extent to create innovative engagement strategies, they can provide a seamless lifestyle experience for that customer in all interactions.1 We foresee this as the next wave of innovation in customer experience—one that demands a deeper, more continuous connection with customers and weaves both the brand and products into their everyday lives. As an added step, businesses can move toward

selling services that enhance the customer experiences with his/her products and connect the customer to additional purchase opportunities. For more information, see our accompanying point of view, “Seamless Lifestyle Experiences: Moving from Transactional Moments to Top of Mind.” Although technology is the enabler of the customer genome, it can also present challenges in terms of collecting disparate data sources and conducting analytics in real time. In the next section, we examine the architecture of a customer genome to describe how businesses can use it to improve their customer engagement and interaction strategies.

Figure 4: Close-up on a customer genome showing the raw data used to derive customer traits such as their preferred engagement channel (online) and interests (technology), which businesses can use to develop customized offers and experiences. The Economist Open Source Software

Slim-fit Non-Iron Bold Gingham Shirt Slim Stretch Cotton Shirt (blue)

Shirt blue

Slim-fit Non-Iron Multi-Gingham Shirt

Shirt non-iron

Slim-fit Non-Iron Open Check Shirt Shirt plaid

Soft-wash crew-neck long-sleeve tee Slim-fit Soft-Wash Green Plaid Shirt Slim Plaid Shirt

Shirt solid

Limited Edition Slim Shirt- Tonal Trim Transactional

Engagement Channel: Online Discount Rate: 26%

Slim Chambray Shirt Shirt size: M

Slim Stretch Cotton Shirt (black) Sunglasses Harvard Business Review Apple, Inc. Amazon.com Amazon Kindle

Shirt fit: Slim

Business/Economics

Technology

Shirt collared/dress Wine

Ubuntu

Engagement Channel: Online Discount Rate: 26%

Age: 27 Reno Wine Walk Gender: Male

Technology

Crime and Punishment Ethnicity: White

Facebook

C.S. Lewis To Kill a Mockingbird Hamlet Fiction Of Mice and Men The Grapes of Wrath The Great Divorce The Time Machine The Once and Future King Sci-Fi/Fantasy Literature The Chronicles of Narnia Beer Alamo Drafthouse San Francisco

Literature Food & Beverage

Slim Chambray Shirt

Lord of the Rings

Sci-Fi/Fantasy Movies

Slim Stretch Cotton Shirt (black)

Serenity Star Wars Indiana Jones James Bond Jason Bourne Gladiator South Park Family Guy

Action movies

Sunglasses Harvard Business Review Apple, Inc. Amazon.com Johnston and Murphy Amazon Kindle

Animated comedy

Lucky Brand

Shirt size: M Shirt fit: Slim

Business/Economics Movies & TV

Men's Clothing/Fashion

Shirt collared/dress

Mid-Tier Fashion

Engagement Channel: Online

Wine

Ubuntu

Jos. A. Bank Express Demographic

Age: 27 Banana Republic

Luxury Brand

Discount Rate: 26%

Reno Wine Walk

Theory

John Varvatos

Gender: Male

Burberry

Crime and Punishment

Technology

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Building blocks of a customer genome The customer genome is designed to provide a holistic view of the customer. Businesses will need to use technology to gather and aggregate the many data touch points to create this view.

Figure 5: The genome architecture consists of four main parts.

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IDENTIFY DATA

Traditional data sources Traditional data sources are currently used by many businesses to create marketing campaigns. Examples include data from internal customer relationship management (CRM), enterprise resource planning (ERP), ecommerce, relational database management system (RDBMS) warehouses and other enterprise systems. These sources yield demographic information, point-ofsale transaction details, loyalty card data, customer survey results and more that can be used to start the customer analysis.

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1. Identify data The first step is to identify and select the data. (See Figure 5.) The customer genome concept is an evolution of current customer and market segmentation practices; therefore, businesses should use both traditional and alternate data sources to develop their customers’ genomes. Looking at these data sets in fresh ways will produce new customer insights and help to provide a more personalized, engaging experience for customers.

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External traditional third-party customer and market data sources are also available through companies that specialize in providing these services. Options include Experian Information Solutions, Inc. household, demographic and segmentation data; and Dun & Bradstreet, Inc. business firmographic data. Compiling this information into a single view and running analytics on the dataset will generate the outline of the customer genome: gender, purchase history, birthdate, clothing size, preferences and more.

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INTERFACE

Another alternate data resource is indoor tracking technology, including beacon technologies, Wi-Fi triangulation or cell phone signals. Businesses can use this information to understand customer shopping habits or pinpoint micro-location. For example, a grocery store could leverage the data to deliver relevant content and coupons to a customer while he is in the aisle choosing between two brands of food. For more information, please see our point of view, “Making Customers Digitally and Visibly Accessible at the Point of Decision.”

Alternate data Alternate data refers to data not commonly used today for segmentation and personalization, as well as data found beyond business borders like social media, community forums and location-based information. Top sources include Facebook, Inc., Twitter, Inc., Pinterest, Yelp Inc., Trip Advisor LLC, third-party product community forums (such as MacRumors.com, LLC.) and other popular consumer sites. Mining these areas for insights, through either social sign-on or web crawling, will help businesses derive insights to better understand the behavior, attitudes and opinions of individual customers.

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2. Ingestion

3. Analytics

In order to achieve this step, businesses need to assimilate their selected data types into an analytics system and prepare to process the data collected from potentially hundreds of thousands of daily customer interactions on a real-time or as-needed basis. Choosing the right platforms to process and house data of different object types, incoming velocities and access frequencies (such as streaming tweets from Twitter and batch uploads from CRM) will require careful data architecture planning. Ideally, a business would establish an enterprise data supply chain that accelerates data movement, processing and interactivity—enabling decision makers to more swiftly capture and act on insights from the data, as well as achieve returns on analytics investments.2 (See Figure 6.)

The next step is data analysis to detect attributes and lifestyle information and to construct a customer’s genome. By conducting analytics, businesses can generate derived data that leads to a variety of useful inferences about the customer that can be placed in his/her genome. Choosing the type of analytics to run will depend on the specific business case to be achieved. Potential approaches include mining a specific customer’s transactional and social data to derive information about how she prefers to engage with a business, as well as her price sensitivities, favorite channels (online, mobile and in-store) and potential influencers (celebrities, brands, family or friends).

In this phase, businesses should be mindful of which analytics need to be run and when, then correlate that with the cost of running the analytics. In some cases, only certain aspects of a data source will be best utilized in real time or will be needed at all. For example, based on business need and priority, a company may choose to create customer genomes for a sub-set of the customer base, such as customers who have not purchased products and services in the past year. From this, they can develop individualized marketing campaigns to rejuvenate the customer base. Another business may choose to create customer genomes for the top 20 percent of customers and grow that segment by identifying “lookalike” prospects and tailoring experiences for those individuals.

Figure 6: Sample hybrid data architecture solution for accelerating data.

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One way to apply the newfound knowledge from analytics is to customize a web site landing page or content on a mobile app. The new view could include filtered product recommendations or provide the customer with a relevant call-to-action. For instance, an apparel retailer could create a customer genome describing a customer’s clothing size, fit, height, body shape, color preferences and current wardrobe. This could be used to provide a more personalized experience when the customer visits the retailer’s web site, such as only displaying shoes in the customer’s size and budget instead of showing the entire inventory. The customized view adds value to the customer experience by eliminating the frustration of an overwhelming number of choices. However, this is just the beginning of what businesses can do with the customer genome. For example, new options emerge if businesses use product information—traditionally designed only to track inventory—in innovative ways, converting it into a rich source of information by enhancing it with attributes and linking them to customer preferences. For example, as shown in Figure 3, an individual customer typically buys slimfit shirts at an average discounted rate of 26 percent; therefore, a business could notify the customer of a sale on similar or complementary items at a retail store.

4. Interface For this step, businesses will need to examine and understand the insights resulting from the customer genomes in aggregate. While the Sankey diagram depicts one customer genome, a business will want to look at multiple customer genomes at one time to identify patterns, clusters or behaviors within the dataset. One powerful new way to do this is to use data visualization technologies as the presentation layer, or interface, to create dynamic views of genome clusters or specific personas. (For more information, see our accompanying point of view, “Why Big Data Needs Visualization to Succeed.”) For example, a Marketing department might select from the data visualization the cluster of “fiction” and the persona of “wine” to create a subset of customers with these interests, then develop a highly customized marketing campaign targeted at individual customers. Businesses can also use a data visualization to identify connections between customer genomes and the products that they like, such as clothing, music or books. These links can be the basis for developing recommendation strategies, as well as online or physical store layouts, to introduce selected genome clusters and individual customers to similar products.

Another way to use the analytics is to understand how customers prefer to engage with brands; for example, customers might like and share products, but not comment on them; or they might prefer sweepstakes over coupons. In this case, businesses should work towards increasing customer loyalty by appealing to an individual customer’s needs and preferences, rather than applying the same call-to-actions for all customers.

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Return on investment for the customer genome Using customer genomes, businesses can design a range of unique, highly personalized customer experiences. Possible areas to apply customer genomes include:

Contextual enhancement Contextual enhancement provides customized content based on how and where a customer is accessing information. For instance, if a customer looks up flight information while at a hotel, a transportation company could display an advertisement for taxi service. When the same customer checks the flight information at the airport and learns of a delay, a nearby restaurant could display an advertisement for 10 percent off lunch items. Contextual information can also be used to improve online, mobile and in-store customer interactions by providing service that adheres to their preferences or developing tailored promotions to secure the sale. A business could use weather data, for example, to recommend outdoor activities on a sunny day and indoor shopping experiences on a rainy day.

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Online

Point of decision

Online offers a tailored customer experience based on usage and engagement, such as a coupon for a price sensitive customer. Internet browsing behavior data (time on site, click sequences, preferred methods of purchase and couponing) can also be used to make inferences about preferred channels of interaction and identify products for which customers are searching.

Customer attention, while inside the store, is a valuable asset that is underutilized. Currently, promotions are delivered to customers at the point where they have the least chance of being successful. Rather than providing coupons after checkout, suppliers and retailers should deliver promotions at the most critical point in the purchase process, the point of decision. Further, those promotions should be personally relevant to the customer, delivering promotions to the customer’s mobile phone in a virtual layer on top of the physical shelf space. Using the customer genome, retailers can present personalized messages to customers in real time and at the point of decision. For more information, please see our point of view, “Making Customers Digitally and Visibly Accessible at the Point of Decision.”

Mobile Mobile enables delivery of relevant content to individual customers based on location information, such as texting a coupon to an individual customer for the next book in the series they are reading as they pass by a book store in the mall. Mobile behavior data can also start providing an individual customer’s location patterns, helping a business to understand how the customer’s life is organized on a daily or weekly basis (i.e., gym, work, errands, home during week; hike or sail on weekends.)

Conclusion: Maximizing the customer genome Digital customer genomes offer businesses an unparalleled opportunity to get to know their customers better, which can translate into increased customer engagement, long-term customer retention and multiplied revenue. Businesses can begin work now. By using currently available data sources, they can work toward the ultimate goal of providing individualized products and services to their sophisticated digital customer base—in a continuous cycle that supports their activities and lifestyles.

Key benefits

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Businesses that invest in developing Use customer genomes to better customer genomes are most likely to understand customer behaviors reap these benefits: and purchase decisions, as well as Increase customer engagement products. Companies can use this knowledge to upsell and influence Use data learned from customer customers into buying higher-end genomes to create innovative products, thus increasing cart size. customer engagement strategies. They can also identify genome With this new information, businesses clusters in order to make relevant can support customers’ lifestyles recommendations or organize and activities with relevant products physical or virtual storefronts. and services at the exact moment a need surfaces. Businesses that achieve this can significantly improve customer engagement efficacy and build deeper brand loyalty.

EXPAND POINT OF PURCHASE AND CART SIZE INCREASE CUSTOMER ENGAGEMENT

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Contact us To find out more about this topic, please contact: Serena Cheng [email protected] Chau Dang [email protected] Bryan Walker [email protected]

References

About Accenture

About Accenture Technology Labs

“Seamless Lifestyle Experiences: Moving from Transactional Moments to Top of Mind,” Accenture, 2014. http://www.accenture.com/us-en/Pages/ insight-seamless-lifestyle-experiences.aspx

Accenture is a global management consulting, technology services and outsourcing company, with more than 305,000 people serving clients in more than 120 countries. Combining unparalleled experience, comprehensive capabilities across all industries and business functions, and extensive research on the world’s most successful companies, Accenture collaborates with clients to help them become high-performance businesses and governments. The company generated net revenues of US$30.0 billion for the fiscal year ended Aug. 31, 2014. Its home page is www.accenture.com.

Accenture Technology Labs, the dedicated technology research and development (R&D) organization within Accenture, has been turning technology innovation into business results for more than 20 years. Our R&D team explores new and emerging technologies to create a vision of how technology will shape the future and invent the next wave of cutting-edge business solutions. Working closely with Accenture’s global network of specialists, Accenture Technology Labs help clients innovate to achieve high performance. The Labs are located in Silicon Valley, California; Sophia Antipolis, France; Arlington, Virginia; Beijing, China and Bangalore, India. For more information, please visit www.accenture.com/technologylabs.

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“Data Acceleration: Architecture for the Modern Data Supply Chain,” Accenture, 2014. http://www.accenture.com/us-en/Pages/ insight-data-acceleration-modern-datasupply-chain.aspx

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