Market researchers and their clients face several challenges when incorporating data from multiple sources in order to craft a usable set of action plans. Yet without first creating a plan on how best to harvest the findings from multiple data sources, action plans for the organization may end up suffering from analysis paralysis. This puts researchers in the uncomfortable position of having to justify even more research when prior research initiatives sit on the shelf without being implemented. This article will outline strategies for taking full advantage of market research initiatives from multiple sources in order to create comprehensive action plans that have real results in real market scenarios.
Overview of Issue
Market researchers work in various industries with clients in a multitude of market segments. The one thing we all have in common is that we all have customers! They may be internal stakeholders in our organizations or external clients who have their own internal customers. Either way, we are regularly faced with the challenge of presenting market research findings in ways that enable clients to reach their business objectives.
Often, this involves integrating results from several sources into a unified package of action plans. Many pertinent findings may be evident among prior projects but not all are necessary to the main objective as this image illustrates.
This Florida sign does an effective job of warning passers-by that caution is needed and that the sign has sharp edges which should be avoided. These are worthwhile messages, but they pale in importance to the footnote that alerts passers-by that the bridge is out ahead! The message with the greatest impact is lost in the fine print.
Researchers also encounter situations where we need to pick through various findings in order to find the most pertinent information. Without developing a cohesive and sensible plan to guide the way, we may find ourselves on the road looking out for signs with sharp edges and fall off the bridge.
The best analysis plans aim to link Voice of the Customer (VoC) input to other feedback mechanisms and internal data systems as part of the progress toward integration.
Conceptual and Practical “How To”
Finding the true North in this forest of information requires defining which business objectives the research is to support. Is it sales growth, cost savings or risk reduction to name a few? Priorities must be set from among various choices; consideration of available business solutions must also be incorporated. The action implementation plan is not likely to be adopted if the proposed solution is not realistic in the current business environment.
Organizations tend to do fairly well with these first two tasks and then stumble when looking at the challenge of incorporating data from multiple sources. Each data source may have several challenges associated with it:
- How complex and complete is the data?
- Where is the data located?
- How is the data formatted?
- What security, quality and governance issues are associated with the data?
- How does the data illustrate the organization’s performance on the key business metrics being explored?
Making sense of the different sources of data requires taking an inventory of the available sources of information. Structured and unstructured data needs to be sorted into categories:
- Financial data about the organization
- Specific data about customers
- Specific data about employees
- Information about operational issues
The diagram below illustrates how one organization parsed their data. They formed four broad categories and drilled down into the details to create a data paradigm to describe their organization.
The integration process begins with understanding the data, identifying key variables and formatting it into a usable structure. Look for commonalities among the data sets, being as granular as possible. Select a unit of analysis such as company, location sites, individuals, households, customer groups, product groups or others.
When integrating data, certain variables may have holes where information may not have been captured consistently. Determine what level of accuracy will be acceptable to your audience and how best to handle missing data from a statistical point of view.
Each source of closed-ended data has specific identifiable variables. Using these variables effectively requires identifying each customer in each source of data. If several identifiers are available, locate or develop a master set of instructions on how they link to each other. Determine whether each source of data has at least one of these identifiers. Identify other available options for linking the sources if primary indicators are missing. Locating these linking variables is critical in being able to effectively merge the data from multiple sources into one analysis file. (Table, below.)
Companies do not need to invest in a completely new Customer Relationship Management (CRM) system in order to effectively integrate multiple sources of information. The primary need is simply a thorough understanding of the information available in each of the diverse sources available.
Getting the Answers You Need
After cleaning and formatting the data sets separately, use a statistical software package with merging criteria to combine the data sets. At this point, set up hypotheses, mine the data to test them and analyze the information.
These are some of the most commonly used data mining techniques:
- Artificial neural networks are non-linear predictive models that learn through training and resemble biological neural networks in structure.
- Decision trees are tree-shaped structures that represent sets of decisions, which generate rules for the classification of a dataset.
- Examples include Classification and Regression Trees (CART) and Chi Square Automatic Detection (CHAID).
- Genetic algorithms use processes such as genetic combination, mutation, and natural selection in a design based on the concept of evolution.
- Nearest neighbor method classifies each record in a dataset on a combination of the classes of the K record(s) most similar to it in a historical data set (where k ³ 1).
Rule induction involves the extraction of useful if-then rules from data based on statistical significance. Complexity science is based on Chaos Theory of Mathematics and is used to ferret out relationships between unrelated variables.
This simple example involving Net Promoter analysis provides an initial look at which segments are most likely to recommend a company to their colleagues. (See Net Promoter Results by Segment, previous page, bottom.)
In order to get a more holistic view of these customers, examine key drivers by overlaying them on the various sources of data. Positive performance can increase willingness to recommend while negative performance has the opposite effect. Determining which drivers have the greatest impact will help guide you in best serving your customers. (See Key Metrics from Various Information Sources by Segment, top.)
Linking information from different sources can also assist in identifying areas of concern. For example, if Detractors experience slower deliveries (from primary research information) the larger their orders are (from financial information), they may be less willing to recommend the company. Correcting these issues can be demonstrated to have a quantifiable impact on the company’s bottom line.
Illustration of Applicability Using Four Case Studies
Acquisition – Growing Market Share
This client is a billion dollar manufacturing company whose equipment is used to transmit data, video and voice signals. They offer network access/transport systems and equipment for tenable carriers to build fiber-optic backbone networks. Customers include incumbent local telephone carriers, cable operators, corporations and government agencies. Their current market share was about 10 percent while the market share leaders enjoyed 3 percent and 25 percent, respectively. The organization reported having difficulty growing market share over their competitors. Although the CEO supported VoC research, middle management provided only lip-service to the concept and senior executives initiated no clear vision or strategy.
This company was using two methods of capturing VoC information:
- Over-arching strategic customer loyalty research program running quarterly
- Monthly transactional survey for customers who had these types of service experiences:
- Recent installations
- Calls into customer service for hardware or software support
- Billing issues
- Recent “up-sells” or upgrades initiated by business development staff
We were asked to assist with the deployment and integration of VoC research results into the organization. Developing a holistic data review was part of this process. Historically, resources were not allocated for action implementation and internal financial incentives to change were not instituted. Furthermore, since the organization grew from entrepreneurial roots, they relied heavily on conventional wisdom for decision-making.
Research data clearly indicated areas for improvement and the “research guy” became the “champion of change,” instituting a data dashboard of key metrics to educate executives all the way up to the board level. In addition, he identified middle manager owners for process change and worked to secure their cooperation. The CEO began to apply more top-down pressure.
Our involvement was to oversee data integration and mining, conduct senior/middle management education workshops, steer action planning sessions at all levels, conduct quality functional deployment input sessions, deliver instructional sessions on how to interpret research data and implement action plans based on the results. We were invited to participate in the strategic planning process and development of specific action planning targets.
The results were quite noticeable! Customer-focused improvements were internalized and implemented, resulting in steady growth in market share. In the first 12 months, the company logged a gain in share of 2 percentage points. In the second year, share increased by another 2.5 percentage points. Currently, share is up yet another 3 points! This overall gain of 7.5 percentage points in market share in just three years is in sharp contrast to flat market share growth in the previous five years.
Service – Improving Performance
This billion dollar company is the leading provider of personal finance, small business accounting and consumer tax preparation software for consumers, accountants and small businesses. Other software offerings include industry-specific accounting and management applications for construction, real estate, retail and wholesale distribution organizations. They also provide payroll services, financial supplies and software for professional tax preparation. This organization has three main business segments: small business, tax and financial institutions.
The company was organized into discrete business units, but VoC listening occurred at the overall corporate level. Corporate leadership sponsored VoC and left it to each individual business unit to implement change. The company wanted one overarching loyalty measure that would define the overall direction of the organization. At the same time, this loyalty measure would have to meet the needs of the individual business units for more information to diagnose product and service performance issues.
The CEO mandated corporate VoC which had a great deal of visibility at the topmost levels of management. However, the needs of each business unit varied greatly and no integrated data solution was being used. Furthermore, no training in continuous improvement methods had been used historically and no plan was in place for appropriate use of VoC data to improve service performance. In addition, VoC data had not been linked to operational processes and standards. Consequently, process improvement goals had not been set and employees had no incentive to implement changes.
Here, we were engaged to communicate the importance of VoC data, improve the understanding of the data and educate employees about its impact on service improvement. We reframed the existing VoC data with the overlay of information from other internal sources. Training sessions on continuous improvement methods were established, teaching individual business units best practices on how to incorporate VoC data into their strategic planning process. We implemented ongoing customer requirements education at the senior-most level of the organization and created processes for the release of funding to provide employee incentives for implementing service changes.
Subsequent VoC tracking waves indicated significant improvements in customer loyalty driven by improvements in product and service performance. Ratings on specific key metrics improved by 15–25 percentage points. This was possible due to the integration of VoC data being linked to internal business process metrics. The organization is now working on translating these improvements into a 10 percent annual improvement in profitability.
Growth – Eliminating Barriers
This organization is a billion dollar data storage company that owns the lion’s share of the market. They make tape drives and automated cartridge libraries, disk arrays and network management/backup software that help businesses and government agencies store and manage large amounts of data. They also offer storage networking products, including third-party hardware devices. Their services range from maintenance and support to consulting and design. The organization sells directly and through Value Added Developers (VADs)/Value Added Resellers (VARs). They have recently been acquired by a technology company whose specialty is servers and routers.
Despite being the market share leader, company profits had been weak over a number of years due to flat growth. Turnaround efforts included a management shake-up, layoffs and spin-off of its managed storage device business. The company remained committed to its core tape storage products, placing increasing emphasis on selling complementary networking and disk-based storage devices to form Storage Area Networks (SANs). Company executives wanted to increase new customer acquisition.
The growth strategy involved many problems:
- Overall resistance to change within the organization was high.
- Business units operated in silos and through functional domination.
- Very few within the organization knew how to use primary research data.
- Buy-in or commitment to making changes was not easy to secure.
- Due to past deficiencies of information, no direct link had been established between VoC and business results, resulting in no data integration.
- Executive leadership also contributed to the challenges:
- The organization had just named a new CEO to replace the top executive.
- The former CEO had systematically disassembled all market listening posts including market research and competitive intelligence.
The new CEO, however, was committed to reestablishing VoC research and other market listening initiatives although incentives for product/service improvements had not yet been established and resources were limited in terms of action implementation. The company was focused on staying afloat.
Again, we were brought in to incorporate the results of VoC into the day-to-day operations of the company. Our first step was to understand all data sources and develop an integration plan. We established VoC listening posts at both strategic and tactical levels and developed intervention strategies through informational sessions, direct hands-on workshops, and direct communication with the new CEO regarding strategic planning. Our efforts centered on breaking down barriers and resistance to change.
Success came in stages. Our major accomplishment was the establishment of cross-functional implementation teams who translated customer requirements for performance into market-focused process improvements. As growth objectives began to be met, the new CEO attributed much of the improvement to better alignment of functional processes with external customer requirements. This was a direct result of data integration efforts. Revenue has grown year-over-year by at least 10 percent for the last three years and net profits have increased 4–5 percent year-over-year.
Retention – Reducing Churn
This organization is the result of a merger between two leading companies. The combination created a giant that aspired to take on the two market leaders. The company operated a nationwide customer network with more than 50 million subscribers and was valued at $35 billion. Since the merger, the combined company struggled to drum up and retain new customers and used layoffs in two consecutive years to cut costs.
The merger resulted in technical snags due to each organization using somewhat different technology. The integration of the two companies proved more difficult than anticipated, and the resulting quality problems contributed to customer defections. This company suffered a notable loss when it was excluded from bidding on government contracts worth billions.
The new organization conducted a monthly VoC monitoring program among a myriad of custom research studies. The data appeared to show little movement in the overall indices used to monitor customer loyalty. All levels of the organization supported VoC listening post initiatives; however, the data was not always available to key decision makers at the right times. Furthermore, the data was also used as a stick rather than a carrot. While management supported VoC, no follow-up for accountability or action implementation was established. In addition, understanding about continuous improvement methods was low. The implications discovered in the data were not clear and provided no strong guidance for reducing churn. Lastly, VoC data was not linked to any other data source or internal business process metric.
We were able to review end-to-end research processes to identify potential areas for improvement and change so that data would be more user-friendly and available for action planning. Part of this process involved a data integration review. We developed communication schemes and conducted workshops on how to gain access to the data and what to do with it once it was available. Action planning and deployment activities incorporated information and actions into the appropriate functional areas. And again, we were invited to participate in the strategic planning process and put forth an ongoing plan of data integration as the company continued to restructure and grow.
During the fiscal year following the process improvement implementations, the overall information services budget had been reduced by 27 percent through a more consolidated approach to running primary research. This research was now directly linked to internal business process metrics. While churn continues to be an issue for this company, the main bleeding of customers has been alleviated. Churn rates have slowed into the single digits from the mid-double digits.
These case studies provide illustrations of how integrating data from multiple sources can help to achieve primary business objectives such as acquiring new customers in order to grow market share, improve performance, eliminate barriers to share-of-wallet growth, and reduce churn.