How Simulation Can be Used to Augment Your Customer Analytics
Customer analytics is an expanding field as more and more data becomes available to organizations on what their consumers are buying, watching, reading, posting, and saying about brands, products, and services.
Data related to customer analytics comes in many forms – CRM databases on customer interactions, loyalty card data on customer purchase patterns, media habits and exposures for different types of customers, survey data from consumer panels.
All of these data sources provide ample opportunities for exploring and learning more about strategies to employ to help a brand grow.
Just looking at this data can help characterize and identify a number of descriptive characteristics about customers. Some examples of these types of descriptive analytics include answering the following questions:
- What percentage of our current customer base shops both online and in-store?
- How many people saw your latest TV campaign and about what percentage reacted favorably to it?
- How frequently do our customers visit our site? Are they likely buying competitor products as well?
- What are customers saying about our latest product feature?
Mining and analyzing a large range of these data sets can provide a lot of useful insight for decision makers. However, another challenge altogether is molding all of this customer data into a strategic plan for attracting and retaining customers. Simply describing the customer is not sufficient to answer key strategic decisions.
As a basic example, you might know what percentage of the population saw your brand’s latest TV campaign, but that does not by itself provide you insight into the return on investment of that particular campaign. Answering that question requires integration of information on marketing, consumers, and sales. Such integration is not carried out by simply looking at descriptive components of customer analytics. However, simulation can be used to augment customer analytics and data to answer broader and more strategic questions.
Simulation can be used to augment customer analytics and data to answer broader and more strategic questions.
Two examples of how simulation can be applied to answer more broad and strategic business questions include:
- Customer Loyalty: Understanding what factors are contributing to customer loyalty, in order to better affect customer retention.
- Cross-channel Effects: Understanding how marketing channels impact individuals in order to produce synergistic effects, in order to construct marketing strategies that attract more new customers.
Customer analytics may describe what percentage of your customers are loyal to your brand, product, or service. However, it does not describe why customers engage in a repeat purchase. The following section describes how simulation can be used to understand drivers of loyalty:
Data on individuals’ purchasing behavior has become more widely available over the past decade. This individual-level data has provided many analytic opportunities to further explore consumer behavior. Tying insights on consumer behavior to aggregate sales outcomes will allow marketers to better understand both short-term and long-term effects of marketing.
One key factor that impacts the long-term effects of marketing is the degree of loyalty that consumers exhibit: how much do consumers tend to repurchase the same brand as opposed to going astray and switching between brands? However, simply quantifying repurchase and switching percentages may not be sufficient to guide a marketing strategy.
It’s important to identify what factors are driving consumers to repurchase a particular brand if the goal is to disrupt the market and change that behavior (get consumers to switch to your brand) or to preserve that behavior in a shifting marketplace (keep consumers purchasing your brand). The first step is to acknowledge that “loyalty” may result from a range of consumer behaviors and mindsets, which are not always related to a consumers’ love or affinity for a particular brand.
[bctt tweet=”Loyalty isn’t always related to a consumers’ love or affinity for a particular brand.” via=”no”]
Below is a list of various factors that may drive repurchase behavior in a market:
1. Inertia – defaulting to a brand out of habit. Day-to-day consumers are faced with many decisions, but everyone has limited time and energy to apply cognitive effort to choose. So often times, consumers take the decision-making shortcut of simply following a routine and taking the path they usually do.
2. Awareness – buying a brand due to the absence of alternatives in mind. In some cases, consumers may simply take path because they do not know of any other way.
3. Pricing – choosing the lowest cost alternative. Many consumers are hunting for the best deal and may choose the most competitively priced option.
4. Perception – staying loyal to a brand that is held in high regard. Consumers may continue to buy a brand because they think it is the best of the alternatives available.
Clearly, there may be much more to “consumer loyalty” than warm and fuzzy feelings about a brand. Perhaps it is human nature that makes many gravitate toward the fourth factor in the list. Most dog owners would hope that their beloved animals find their way home each day out of love as opposed to out of laziness, convenience, or simply because there is no other option. Some brand managers may hold to the same hope about consumers who do not stray from their brand. Hope is not a strategy though. To get the most out of customer analytics, managers need to acknowledge that in reality consumer “loyalty” is driven by a range of factors that may constantly evolve in a dynamic market. This will allow a marketer to succeed in a competitive environment.
Another area of customer analytics related to what marketing tactics different individuals are exposed to and how their behavior corresponds to them. These sort of descriptive statistics allow for identification of how much marketing activity reaches individuals but does not give a comprehensive picture into how that marketing may be working to influence their behavior. This is where simulation can be useful and the following section details how marketing channels work together to produce cross-channel effects by influencing different consumer properties:
Cross-channel effects occur in situations where the sum of the parts is not equal to the whole. These effects can be positive (synergistic) or negative (inefficient).
For instance, imagine that you have two media channels at your disposal: TV and Facebook. Should you run them separately or together? The answer to that question depends on the nature of cross-channel effects between these two channels: whether there is a positive or negative interactive effect from running these two channels together. If the sales lift you get from running these two channels together is different from what you would get if you ran each channel separately and added the sales lift you got from each, then there is a cross-channel effect. The types of cross-channel effects that can occur are defined as follows:
Synergistic Cross-Channel Effect
A case when the cross-channel effect results in a synergy
In this case, the sum of the parts is less than the two considered as a whole. There is some extra lift in sales that results from a synergistic interplay between both channels.
Inefficient Cross-Channel Effect
A case when the cross-channel effect results in an inefficiency
In this case, the sum of the parts is greater than the two channels working together, which has created some inefficiencies.
The cross-channel effect is therefore defined as the difference between the individual lifts and the total lift:
[Lift in Sales from TV] + [Lift in Sales from Facebook] + [Cross-Channel Effect] = [Lift in Sales from TV & Facebook].
The effect of marketing activity on sales is often analyzed using regression techniques using sales as a dependent variable. Cross-channel interactions may be quantified with regression by including an interaction term in the model specification. This analytical technique is useful for measuring the effects of media, i.e., allowing for sales attribution and cross-channel interactions to be computed. But the task of understanding what mechanism actually causes the cross-channel effects remains a mystery.
To fully understand the mechanism that drives cross-channel interactions – we need to recognize that the linkage between marketing activity and sales lies in the mind of a consumer. The consumer’s decision-making behavior is what ultimately drives sales. So how do consumers choose?
Consumer Decision-Making is the Key to Cross-Channel Effects
The growth and acceptance of behavioral economics as a science behavior has been instrumental in allowing us to take some simple rules to create simulated models of consumer decision-making that take into account heuristics and rationality. This knowledge has allowed us to build models to measure consumer behavior that go beyond notions of perfect equilibrium and neo-classical economics that were never originally designed for this purpose.
In simple terms, consumers make decisions based on options they would consider, and select the option they perceive most highly on attributes that are most important to them (based on the behavioral principle of utility maximization). This view assumes a path in the consumer-decision making process as follows:
The components of the consumer decision-making process lead to the different cross-channel effects
This approach means that both brand awareness and brand perceptions are important in driving sales. Some touchpoints may be more effective at building awareness and others at building perceptions. It won’t do the marketer much good to focus on building brand awareness if the product is perceived very poorly – a perception or satisfaction bottleneck that may not be solved by throwing more media at the problem. Likewise, it might be unhelpful to attempt to build deeper engagement for a brand no one recognizes – an awareness bottleneck.
Synergies and inefficiencies may result from the awareness stages that exist in the complex non-linear path to consumer decisions. Inefficiency may occur when activities focused on awareness-building or perception-building continue when brand awareness or brand perceptions are already maximized. We may saturate awareness and observe that any additional marketing yields no incremental benefit. A synergy may occur when touchpoints that build awareness and others that build perceptions are used in conjunction. We will get more out of our perception-building media campaign, for instance, if more of the consumers are made aware of the brand.
[bctt tweet=”A synergy may occur when touchpoints that build awareness and others that build perceptions are used in conjunction.” via=”no”]
In short, inefficiencies may occur when we concentrate too much on one step in the consumer-decision making process, and synergies may occur when we balance our efforts between different steps. See below for a mapping of TV and Facebook from our example.
A mapping of two channels on two criteria: how well they affect awareness and perceptions
Building understanding of what drives synergies and inefficiencies within a marketing plan can bring valuable additional insight to strategic decision-making and augment your customer analytics. Thinking about the path in the non-linear consumer-decision making process – and the various bottlenecks and saturation points therein – may allow a team to consider new creative strategies that balance awareness building and perception building activities. Simulations may then serve as an aid to testing these hypothetical strategies prior to implementation to include interim metrics such as brand awareness, brand perceptions, and WOM activity.