Using Marketing Opportunity Analysis
Though some value exists in gathering competitive intelligence, the key to customer-centric marketing is knowing one's own customers -- their behaviors, attitudes and preferences -- and mining customer data in a way that helps build brand, maintain profitable relationships and produce measurable return on investment.
The only way to combat copycat syndrome is to let data drive all marketing decisions. However, because today's sophisticated databases are bursting with data, it is common for retailers to become overwhelmed and, as a result, misallocate marketing dollars.
Developing a marketing opportunity analysis lets retailers use data to increase ROI. This tool is used to deliver action- and customer-focused data that can be applied to improve marketing strategies, identify and quantify new marketing opportunities and assist in campaign planning.
In a sense, it is customer segmentation, but performed with a heightened level of richness and accuracy, based on statistical modeling that takes into account the many metrics available to retailers today that previously were not documented. It's not just recency, frequency and monetary value anymore.
To begin, a descriptive overview of customers and their behaviors is developed to serve as a baseline for strategic decision making. It should include cross-shopping patterns, customer tenure, demographic and geographic distributions and multichannel use (retail vs. Web). From here a "best customer" profile should emerge, meeting criteria relevant to the retail operation, such as total expenditures, categories shopped and frequency.
Deeper analysis then can identify look-alikes, or those who show "best customer" potential. With this data, retailers can seek opportunities and identify high-potential customer segments as candidates for market-stimulation programs.
Critical to data-driven marketing is the willingness to delve deep into data, to quantify and rank opportunities, to summarize key findings, to develop recommendations based on the analysis and, finally, to test, refine and measure results. This ensures the most appropriate allocation of marketing dollars and a measurable return.
Retail success stories have common characteristics. For one, it's likely that such retailers used data extracted from analyses to refine current direct marketing strategies (as well as other strategies, such as site selection, store design and merchandising, not discussed here).
It's also probable that they used their analysis to facilitate future marketing planning and developed new marketing programs based upon identified opportunities. Finally, these retailers establish tracking and measurement reporting infrastructures. As an example, a retailer of children's clothing can create a MOA to help:
· Provide a descriptive overview of its customers.
· Identify high-potential customer segments.
· Profile best customers to use in prospecting.
· Develop a communications plan that can support in-store promotions and niche marketing efforts.
Through MOA, a retailer could determine the average amount spent per visit, know the number of visits and frequency, know the number of items purchased and have a handle on the number and type of departments in which purchases were made. A closer look at a subset of active customers would reveal specifics regarding timing, online vs. offline shopping habits and demographic data such as age, affluence, presence of children, ages of children, marital status and homeownership.
Opportunities for each category then can be explored and marketing program recommendations developed. A retailer also might develop a customer e-marketing strategy to reach this segment regularly, efficiently and cost-effectively.
With a MOA, a retailer would be well-armed to develop many marketing programs including new customer mailings, grand opening announcements, best-customer thank-you letters and "new mom" communications. Performing a MOA gives retail marketers the potential to build long-term customer relationships and increase ROI.