What is Consumer Intelligence?
Deciphering and forecasting consumer behavior by demographic cohort, household profile, generational characteristic, and individual incentive is critical for both strategic and tactical decision making. Moreover, understanding broad trends as well as nuanced shifts in consumer demand allow for optimized timing of decisions. Below is a dynamic illustration of US population by age cohort and by generation from 1980 to 2050. Note how we bifurcated the most targeted demographic group today, The Millennials, into “Early Millennials” (Orange) and “Late Millennials” (Red).
Consumer Intelligence includes the gathering and interpretation of information about target markets and customers. It is the analysis of data gathered across marketing channels in order to evaluate the success of marketing initiatives and it’s resonance with consumers. Consumer Intelligence is conducted using quantitative methods focusing on data, statistics, and analytics as well as qualitative methods such as customer surveys and focus groups. Techopedia.com explains that, “The process of customer analytics examines and captures consumer behavioral data and guides segment markets. By doing so, this process can even suggest future product and service offerings to designated companies.” In their definition of Customer Intelligence, Market Force writes, “Customer Intelligence is a new, emerging methodology for customer experience insight. Just like business intelligence systems summarize fiscal performance data, customer intelligence gives companies a single, financially accountable view of all their customer-related information. It helps you stay on top of your customers, your stores and your financial performance, and calculates the ways in which all three are connected.”
Key Areas of Consumer Intelligence
Consumer behavior analysis
Customer relationship management (CRM)
Consumer demographics, psychographics, and firmographics
Market trends analysis
Marketing / channel / social media analytics
Price vs. value information
Supply / demand analysis
Why do I need Consumer Intelligence?
Successful companies rely on consumer intelligence to support better and ultimately more profitable decision-making related to meeting the customers’ wants and needs. Successful organizations also realize that good consumer intelligence is essential for identifying, segmenting, and understanding target markets as well as knowing the competition and how it’s prospective or current consumers will react to that competition. It is also necessary for identifying the best marketing channels and strategies in order to be the most effective and efficient in reaching current and future consumers.
TDWI explains 4 key reasons organizations should analyze customer behavior, including to gain insight, attract and engage customers, improve customer retention, and strengthen bonds with customers. Below are excerpts from the recent TDWI article:
“Gain insight: Clearly, one of the main reasons organizations analyze data is to gain insight. Exploring your data for insights about customer behavior may involve segmenting your customer base, which often uses cluster analysis, a technique that organizes a set of observations into two or more groups that are mutually exclusive based on combinations of variables.
Typically, organizations do discovery and segmentation analysis using structured data. However, unstructured text data, such as social media data or internal text data, can also provide great insight into customer sentiment and behavior. More often, organizations are performing social media analytics, such as voice-of-the-customer analysis, using text analytics technology to gain insight about what customers are saying and how their brand resonates with existing and potential customers. TDWI sees increasing interest in these technologies.”
“Attract and engage: If you’ve segmented your customer base, you can target customers and engage them because you have a better sense of what they might be interested in. For instance, an organization wants to make customers the right offer when it launches a product campaign across various channels (online, e-mail, mobile, in-store, etc.). By analyzing historical purchases and profiles, companies can predict the likelihood, or propensity, of future activity at a customer level. For instance, a company might use a propensity model and past purchase behavior to gauge the probability of a customer making a certain purchase. This data can be used when developing the new campaign.”
“Improve retention: Customer retention is a key marketing activity, especially when it comes to profitable customers, and predictive analytics can be extremely helpful. For instance, decision trees can be useful where there are discrete target or outcome variables of interest (leave or stay, for example). Typically, a set of historical training data is provided to the predictive analytics algorithm. The data might consist of different kinds of information about customers (demographics, purchase history, even past sentiment) and it is used by the decision-tree algorithm to determine decision rules that describe the relationship between the input and outcome variables. These rules can be used against new data where the outcome is not known (for instance, leave or stay). These models are often operationalized — for instance, in a call-center where agents can use them to try to retain customers at risk.”
“Strengthen bonds with customers: Organizations want to continually strengthen relationships with their existing customers while attracting new ones. Customer lifetime value models help organizations understand the future worth of customers and segments. It is an important part of a customer strategy. Techniques like affinity analysis using market basket analysis to understand combinations of products bought together can be very useful in driving e-mail marketing and recommendation engines.”
Why choose Competitive Analytics to assist with my Consumer Intelligence needs?