Your customers are the key to using Big Data analysis to improve the customer experience, thus driving revenues and profits. Here’s how to begin.
As the world of Big Data analysis explodes, businesses are getting lost in the sea of data. In many cases they are wasting a large amount of corporate resources combing the data for patterns without a clear focus on what they are trying to find. Companies that are adrift in data can use customers like a compass to provide their Big Data initiatives strategic direction.
Successful Big Data analysis starts with a clear hypothesis and stays focused on a business outcome. Ultimately, Big Data analysis should result in increased sales (through better products/services or better marketing) or decreased expenses (through more efficient delivery). The customer experience organization is perfectly positioned to provide the guidance for customer-focused hypotheses development, thus pointing the way for intelligent Big Data analysis. Customer experience can lead the way by:
- setting priorities for more effective root-cause analysis of operational data
- determining which operational metrics to monitor
- informing innovation and marketing
- being a leading indicator during new product introductions
In addition, the customer experience organization has the insight to ensure representatives of all Big Data sources are actively involved in the analysis plans. Cross-functional participation will increase ownership and speed the implementation of the customer-focused changes identified through Big Data analysis.
Successful Big Data analysis starts with a clear hypothesis and stays focused on a business outcome.
Customer experience has access to three types of data that can be linked to provide a clear understanding of the customer problems that must be addressed. This information helps set priorities for more fruitful Big Data analysis of operational data:
- Research data on customer issues that cause the greatest loss of loyalty (i.e., most customers at risk)
- Customer complaints and inquiries
- Social-media comments
It is the combination of these data sources that can provide the greatest impact on setting priorities. First, the research provides an understanding of the impact of customer issues on future sales, e.g., when a customer experiences an out-of-date product, 30 percent are less likely to buy that brand in the future. Second, the research puts the complaint/inquiry data and social-media data into perspective as it relates to the marketplace, e.g., one complaint about an out-of-date product equals 10 actual occurrences in the marketplace. Third, the volume and trends of both traditional customer complaints/inquiries and social-media comments provides information on the frequency and trends of customer opinion and sentiment. These can be combined to help set the direction for further Big Data analysis and pinpointing of the root cause of customer issues that impact loyalty.
In an example with a television-service company, customer survey and operational data showed that it was taking more than one visit to move a customer’s service to a new address. These additional installation visits, which added to the customer’s stress when moving, created negative word of mouth/mouse as well as a negative impact on loyalty. A system-wide investigation into root cause correlated structured and unstructured data across several Big Data sources—both customer data and operational data. Specific improvement opportunities were uncovered and corrective actions were implemented to restore customer loyalty and confidence in the service provider.
From consumer-experience research, a consumer packaged goods company gained an understanding of the “complaint multiplier” (i.e., relationship between complaints and incidence in the marketplace) and the impact of issues on consumer loyalty and identified “split or torn package” as an issue that had a larger than normal impact on consumer loyalty. When the consumer affairs department identified (through both consumer complaints and social media) a larger than normal increase in split package issues, they alerted the quality department. This set off an immediate in-depth analysis of the root cause of the increased issues by product and plant. The root cause was quickly identified as a supplier change in packaging material and was quickly rectified.
Similarly, a bank identified that its mortgage process was causing a high rate of consumer complaints resulting in reduced loyalty to the bank’s other products and increased negative word of mouth through all channels, including social media. Data gathered by the customer service department from a further analysis of operational data allowed the bank to pinpoint the root cause of customer issues and drive a solution.
Determine Which Metrics to Monitor
Customer experience management often has information on the key drivers of customer loyalty or which issues cause the most loss of loyalty. This can help set priorities for ongoing operational metrics, thus increasing efficiency, since not everything needs to monitored or analyzed on an ongoing basis. Additionally, organizations often use surveys to monitor performance that they can more cost effectively monitor with ongoing operational metrics, according to TARP’s white paper, “Stop Wasting Money: Don’t Use Surveys to Get Answers You May Already Have In House.”
A quick service restaurant determined that “incomplete orders” had a bigger impact on customer satisfaction and loyalty than timeliness. This resulted in a more rigorous order check system.
When an investment company surveyed its clients, many reported that fund redemptions took too long. After documenting the redemption process, Big Data analysis was developed that leveraged several operational data bases as well as client and supplier data. Critical process improvement opportunities were implemented, and ongoing operational metrics were established to ensure redemption turn-around time exceeded client expectations.
Cross-functional participation will increase ownership and speed the implementation of the customer-focused changes.
Informing Innovation and Marketing
One of the most frequently mentioned purposes of Big Data analysis is product/service innovation and identifying new markets/market segments. Customer experience management can provide a starting place through analysis of customer complaints/inquiries and text analysis of customer verbatim comments from both customer contacts and social-media mentions.
For example, an appliance company conducted extensive data mining of consumer comments to better understand how consumers used appliances, what they found difficult to use and what types of words they used to describe great experiences. This provided the direction for more in-depth ideation sessions and market analysis, leading to significant increases in successful innovation.
A further analysis of customer data can reveal new uses of a product, leading to product innovation, or unexpected demographic make-up of users, leading to enhanced segmentation and targeted marketing.
New Product Introductions
Customer experience data is a leading indicator of the success or failure of new product sales. Analysis of the velocity of social-media comments and customer complaints/inquiries during the introductory period can identify areas where a new product/service introduction may be going awry, thus pointing the direction for more in-depth analysis and corrective action.
Customer data played a significant role when a telecommunications company needed to get a new product launch back on track. In one example, an analysis of a high volume of survey comments identified a severe and frequent customer problem that threatened the success of the new product launch, and so required a rapid response. In a Big Data initiative that spanned multiple sources, network engineers linked plant and equipment activity data as well as telephone traffic data to customers and their survey feedback. Over time, with continued investigation linking operational and customer data, the problem incidence dropped by more than half.
Increasing Your Success
As a customer experience professional, you know your department is sitting on a wealth of data that can have a significant impact on setting priorities for Big Data analysis. But others in the organization may not understand the connection and somehow see the customer experience data as less important. Here are five tips for increasing your success in this area:
- Understand how customer experience data relates to the marketplace, operational data and the business:
- Know the ratio between complaints and incidence in the marketplace
- Understand the impact of specific issues on customer loyalty
- Understand the demographic make-up of contactors and social-media posters and how it compares with the overall demographics of your customer base
- Learn about other data sources and how they are analyzed and used for corporate decision making:
- What are the ongoing operational metrics: How are they collected, with what frequency, how are they used?
- What are the key metrics on the corporate scorecard and weights applied?
- What is the innovation process?
- Build bridges in the organization. Involve other people as they learn how to see from the customer point of view and build the customer into their analysis.
- Identify operational data that can serve as a proxy for critical customer measures, and build them into your scorecard system.
- Consider starting your work in Big Data with a pilot project to develop and fine-tune the steps that could be used in larger, more critical initiatives.
Customer data played a significant role when a telecommunications company needed to get a new product launch back on track.
The customer experience organization can help ensure that the results of Big Data analysis are used to improve the customer experience, thus driving revenues and profits. Three of the eight best practices for an effective voice of the customer process identified by TARP’s research, “Best Practices for Creating a Cost-Effective Voice of the Customer Process” that have a direct impact on ensuring action with Big Data analysis are:
- Identify the cost and revenue implications of the opportunity. Quantify the identified opportunity in terms of increased revenue, word of mouth and costs avoided. Conservative estimates vetted with the finance department are most effective.
- Ensure there are formal corporate processes to ensure that data are translated into goals and actions. Identify the operational metric that should be monitored to track an improvement, the current level, the goal and the executive who is accountable for the goals. And then track the progress in a highly visible manner.
- Track the effectiveness of the Big Data analysis effort. A prevalent weakness is that companies often fail to clearly ask if the results of the Big Data analysis is clearly making a difference in terms of increased revenue or decreased costs; in other words—measuring the return on investment of the Big Data analysis effort. While this can’t rest on the shoulders of the customer experience organization, you can ask the questions to help steer the organization.
Big Data is a big opportunity for businesses to cultivate deeper connection with their customers, to provide innovative products and services, to enhance product delivery and reduce costs. Companies should use customers as a compass to steer their Big Data initiatives in the right direction.