Cannibalization can be defined as gain in market share of one brand at the expense of another brand/s in a company’s brand portfolio. Companies usually study cannibalization by measuring the sales volume trends of various brands after the launch of a new brand.
But does cannibalization start as soon as the consumer exhibits brand switching behavior or even before that? It starts manifesting itself when the manufacturer asks the retailer to stock the new product. The new product launch gets a priority and is at times stocked even at the expense of other brands.Hence to study cannibalization what should also be evaluated is the jostle amongst various brands to maintain their shelf space. Quantity of products stocked on the shelf not only showcases the push that the company is trying to create but also the potential effect on sales of other brands that are getting nudged off the shelf.
Before arriving at definitive cannibalization conclusions, the relation between shelf spaces and counter sales for key brands should be studied. In-store merchandising and product placement significantly affects sales of new brands (as has been studied by several researchers). Based on the placement-sales association, decision should be taken to de-prioritize display of certain brands and the vacant space should be allocated to a new brand. This should to a large effect discourage the new brand from eating into the sales of existing core brands. After all the performance of a new brand should hinge on whether it can help the manufacturer gain overall market share and not on it barely realigning the brand share of different products (unless of course the new brand is a product extension (such as Mach 3) or is launched to kill an existing brand or is launched to make it the core brand).
Webmetricsguru points to two interesting studies done on impulse buying. One of the studies uses a behavioral economics framework (with brain scan evidence) to explain the buying process.
..consumers trade off the immediate pleasure of making a purchase against an immediate pain: the pain of forking out the money for the item.
Furthermore, to increase the likelihood of ‘impulse’ buys, online merchants are suggested to follow some principles which are very analogous to the ideas we have put forward in our recent Choice Optimization paper.
- Expose the target audience to the “stimuli”:
Priming the audience through targeted advertising and product placement
- Figure out what the price point for buying “stimuli” items are for the target audience(s):
Avoid price risk for consumer through market research, transparent pricing (showing competitor prices like Progressive) or price matching schemes
- Offer the item(s) (stimuli) at a lower price than what the target audience thinks it’s worth:
Relative framing by comparing the value of the decision compared to an alternative.
To make the point in tangible terms, here is an interesting example of relative framing which we found while researching for the whitepaper:
The Economist magazine once offered its three subscription options on its website,
(a) Online only for $59
(b) Print only for $125
(c) Online and Print for $125.
16% of customers chose the “Online Only” option and 84% selected the “Online and Print” option. No customer chose the “Print Only” option, so the company removed it. However, when only two choices were presented (“Online Only” and “Online and Print,” at $59 and $125, respectively), the number of customers choosing the lower priced “Online Only” product increased to 68%, while the percentage of subscribers choosing “Online and Print” dropped to 32%. While no one chose the “Print Only” option, having it available made the more expensive “Online and Print” option appear to be a bargain, and this drove a higher percentage of customers to select it.
Photo credit: PaysImaginaire
Professor Dan Ariely of MIT has done extensive research on human decision making using the framework of behavioral economics (go here to pre-order his forthcoming book on the subject). We recently collaborated with him to apply some of his research ideas in the online world. The result of the collaboration is the white paper: Predictably Irrational Customers: Optimizing Choices for How People Really Buy, Not How We Think They Buy.
As customers begin to make more financial decisions online either by conducting a transaction or researching a product online before buying at a store, it becomes critical for companies to have framework to understand customer decisions to generate potential ideas to improve the website. This coupled with the ability to rapidly baseline, test and evaluate the ideas using web analytics makes for a potent capability for companies to gain competitive advantage.
You can download the white paper here
PS: You can also set up sometime to speak with our resident choice optimization experts by sending an email.
Customers calling call centers don’t want long waits and often perceive them as longer than actual.
At the Intelligent Transportation Systems Lab of the University of Minnesota, an experiment was designed where highway driving conditions were simulated in a lab (a Saturn was peeled off and a 270 degree screen in front provided the video). It showed how people value time under different situations. While almost all the respondents remarked that they had actually ‘driven’ for a shorter time than the actual simulation, they also noted that they had waited on the ramp before entering the freeway for 30 seconds or more when the actual controlled waits ranged between 5 and 12 seconds.
A vital component of interaction between customers and service professionals is the time it takes to respond to the customer’s needs and requests or more importantly, the perception of such service times. Adaptive queuing theories can play an important role in improving perceptions of customer service. It is non-ideal to keep a customer waiting for a long time when their needs are fairly straightforward. And in such cases they value their waiting time even more than they actually waited for, in some cases, many times more. While such experiences may be fairly commonplace and their pain all too well-known, businesses, save for exceptions, rarely apply adaptive queuing theories to correctly predict and plan for servicing an individual. Disney engages customers in distractions while they wait for 45 minutes in a queue for a 3 minute ride. Situational elements like music, lighting, color, employee visibility, social interactions, visible queue movement, number of counters serving vs. idle, a visible clock nearby all influence perceived time. Companies can benefit by putting more thought into the psychological aspects of waiting times and queuing while trying to increase service levels and influence customer satisfaction.
