JUNE 2018 | HARNESSING DATA AND TECHNOLOGY
Harnessing Data and Technology
To Optimize The Retail Customer Experience
As retail competition increases, the need to provide an outstanding customer experience (CX) does too. No longer are buying decisions made on price alone. Things like loyalty programs, a satisfying in-store experience, and easy returns are today’s retail necessities. They are the ingredients that lead to a better overall customer experience and factor heavily into customers’ buying decisions.
To succeed long term, it’s more important than ever that retailers create the best customer experience possible. Gartner finds that expectations are growing as to the importance of CX as a competitive advantage. “Today, slightly more than two-thirds of marketers responsible for CX say their companies compete on the basis of CX.”1
Fortunately, the exponential growth in data and digital tools have made it easier for companies to create a better customer experience, drive sales, and increase loyalty. Data mining and journey analytics are used to identify and understand customers’ needs and behaviors. To gain a market edge, successful companies are using both analytics and technology to turn raw data into meaningful actions throughout their customers’ lifecycles.
The following pages outline how companies can leverage data to uncover valuable details about their customers. In addition, mapping the customer journey and strategically structuring the data can also reveal actionable insights that can be used to improve the customer experience across all touchpoints.
1: HARMONIZING DATA ACROSS TOUCHPOINTS
Harmonizing data from a variety of touchpoints is essential to creating a seamless customer experience. One of the challenges is to leverage available tools to capture the data in real-time and then link it to customers’ actions.
Synchronizing data and technology tools provides the answer. The key is to understand how data is stored, analyzed and accessed to maximize the experience. There are two broad categories of data that need to be considered: structured and unstructured. Structured data includes specific information that’s measurable and searchable—things like customer IDs and phone numbers. Unstructured data includes everything from emails, to social media feeds, to texts and photos.
Retailers not only have access to traditional structured data, such as transactions and responses to sales promotions, but also to newer forms of unstructured data, such as consumer reviews, permission-based online and real-time mobile information. All this data provides the opportunity for insights into a customer’s communication and purchase preferences.
The challenge is using this raw unstructured data to provide the best experience for the customer and the best outcome for the retailer. One solution is a data lake—a large-scale data repository and processing engine. A data lake can solve the challenge of integrating structured with unstructured data. The platform provides a range of technologies to ingest, cleanse, and integrate structured, semi-structured, and completely unstructured data in streams.
Data lakes can ingest digital data from a company’s web activity, digital marketing campaigns and DMP (Data Management Platform) to help manage, market and analyze both first-party and third-party data to optimize campaigns. Then, a DSP (Demand Side Provider) leverages those segments to place them into usable marketing formats, like ads, banners and messages.
2: MAPPING THE CUSTOMER JOURNEY
Data is the foundation
Collecting and organizing customer data is foundational to optimizing the customer experience. From the data, we can start to build the full 360-degree view of our customers’ demographics, shopping and spending patterns, online browsing habits, and preferences.
A visual representation of cross-channel customer interactions, touchpoints and behaviors is a helpful tool. This visual map (customer journey map) identifies the customer’s experience and identifies where barriers, inefficiencies and opportunities exist from a customer’s point of view. The resulting customer journey map can be used to discover insights into how to improve the overall customer experience.
The data lake provides the repository from which a customer journey map can be created. By employing the latest techniques, users can extract specific data from the data lake to develop new, more accurate models, iterate faster, and find new solutions or make improvements. Additionally, the data can be enabled to interact in real-time to optimize and/or influence the customer experience.
For instance, based on historical data, predictions can be made about a customer’s preferred communication channel. If email is the preferred channel, the customer would receive email offers instead of direct mail offers. In addition, a customer’s tweets and likes about a product can be streamed into the data lake to help predict their next likely purchase. This could trigger an offer to be sent to the customer for that purchase.
3: Creating the Optimal Customer Experience
Technology makes it happen
While data is the core input, the analytics provide the learnings and insights to enable the technology to implement the appropriate actions. From computers to mobile devices to artificial intelligence, a wide range of everyday technologies can be deployed to interact with the customer at the right time, on the right channel, with the right message.
The purchase journey is exploding with robust new tools and service providers to help organize, orchestrate and interact with customers at every step of their journey. The combined suite of tools, along with data and analytics, allow today’s retailers to learn and leverage their customers’ preferences and habits while also predicting their next action. This ultimately creates a better, more optimized customer experience.
Creating an optimal customer experience is a necessity in today’s retail environment. A McKinsey survey of senior executives reveals 90% of respondents think customer experience is one of the top three priorities of CEOs.2 Clearly, today’s top executives recognize its value.
Data lake technology can help companies by integrating the data and providing a robust suite of tools to quickly develop advanced analytic models. These models can be integrated into the customer journey to drive better experiences and create more engaged customers.
There are millions of data points available to companies today. The value of customer data is realized when retailers seamlessly integrate the information to drive an enhanced customer experience.
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1Source: Gartner, Customer Experience in Marketing Survey 2017: Greater Expectations, Greater Challenges, Augie Ray, Jane-Anne Mennella, et al., 5 October 2017. https://www.gartner.com/document/3812767
2Source: McKinsey & Company. The four pillars of distinctive customer journeys. September 2016. https://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/the-four-pillars-of-distinctive-customer-journeys
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Authors: Kevin Stark—VP, Business Insights & Solutions, Shub Rakshit—VP, Emerging Technology and Innovation
Contributors: Lisa Firestone—VP, Marketing Innovation Insights, Sanjay Sidhwani—SVP, Advanced Analytics, Sue Yasav—VP, Thought Leadership
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