It is very challenging to evaluate a new product launch and cannibalization without clear baseline metrics. In a previous post we talked about measuring brand performance and cannibalization and hinted that the company should use a set of KPIs to measure the success of the launch. Every company and industry will have different factors (KPIs) which are important for the product to meet before it is called successful. This post presents a framework which identifies the attributes of successful product launch against whom KPIs can be developed.

We like the PERFECT model developed by Michael Burkett of AMR Research, PERFECT model attributes of a PERFECT product launch are embodied in the acronym itself:

* Profitable: meets business financial goals.
* Exciting: creates demand in the marketplace.
* Reliable: performs to defined specifications.
* Focused: delivers the most important product in a portfolio.
* Efficient: makes optimal use of available resources.
* Customer-centric: clearly meets defined requirements of the market.
* Timely: delivers at the appropriate time to satisfy demand.

The PERFECT framework is the first step in developing the KPIs and establishing benchmarks during the product planning phase – which can be monitored at regular intervals after product launch to evaluate its performance.

It would be interesting to have a debate on how FMCG companies define the success of a newly launched brand. Well, for the starters, you need to have some targets. What is a right target to have? Something that makes you break even in the shortest possible time? How long should be the planning horizon for such investments? Is there an investment recovery curve that the brand should have? Should the brand have success comparable to some other success story of yesteryears? And most importantly, how do we study the extent of cannibalization caused by the new brand launch?

There are several such questions that product managers grapple with when they are trying to define and measure the success of a newly launched brand and cannibalization effects.  On a recent project done by Diamond, we started researching similar questions and realized that there isn’t much literature available on this topic. Using the limited literature available, from an analytics POV, again, we came up with one such possible approach -

1.       Define a set of KPIs (such as sales, % of sales in first x weeks, brand share in week 1, aggregated brand share in the first quarter since launch, etc.) to measure the health a new brand. Noticeably, brand launch success will not just have metrics that capture week on week growth, etc. that are standard fare for reporting the health of stable brands.

2.       While product seasonality can be factored in, it’s not possible to factor brand seasonality. For instance, while we know that weekends have higher footfalls in retail stores, or poultry product sale is higher during the evenings, etc. we cannot generalize these results for a newly launched brand.

3.       Identify a set of brand launches by the company historically. (Tricky bit: Getting the right data/date for all the brand launches you want to analyze, and the change in data systems over the years)

4.       Study the post-launch sales curves to identify the duration after which brands start imitating the market trend. Let’s call this duration X.

5.       Build an X-week or month model for the product portfolio pre-launch that would forecast company sales in a product category, assuming no new product launches.

6.       Derive the equation for the product portfolio for the X-weeks of data after the launch of a new product.  Keep the current/historical state of the market/portfolio as a key independent variable.

7.       Derive the equation for the new product for the X-weeks of data after its launch. Keep the current/historical state of the market/portfolio as a key independent variable.

8.       Stabilize the equations in 6 and 7 for a set of launches. Each launch adds to the information around how a company has historically managed its launches.

9.       Forecast the portfolio as well product sales for a new launch that happens on a certain date.

10.   Compare the model run results with the actual performances in the X-weeks to come to assess whether the brand launch can be called a success, and whether there has been significant cannibalization

11.   Caveats

a.       Here, fine-tuning the model would require several business driven assumptions around promotional activity, some identified seasonal variations such as 4^th July, product extensions, other launches, competitive activity, etc.

b.      The choice of model is also critical to the market validity of the forecast.

c.       Need to test out the model on several time windows is essential.

d.      It is still debatable whether an aggregated model is better than an aggregation of models, e.g. combining the results from models built for each brand in a certain category (such as energy drinks) vs. modeling for energy drinks as a whole

e.      Finally, validate the results against the KPIs. For instance, a launch classified as success based on model results should also show strong brand KPIs.

There are limitations to this approach as well as the big question around the best metrics to measure the health of a newly launched brand, as any marketer would be quick to point. But let’s see, how many holes can you drill through this wall?

A recent article in WSJ talks about how offline segmentation schemes and data is being used for online targeting of advertisements. The sidebar of the article mentions how Acxiom executes on this strategy

1. Acxiom has accumulated a database of about 133 million households and divided it into 70 demographic and lifestyle clusters based on information available from public sources.

2. A person gives one of Acxiom’s Web partners his address by buying something, filling out a survey or completing a contest form on one of the sites.

3. In an eyeblink, Acxiom checks the address against its database and places a “cookie,” or small piece of tracking software, embedded with a code for that person’s demographic and behavioral cluster on his computer hard drive.

4. When the person visits an Acxiom partner site in the future, Acxiom can use that code to determine which ads to show.

5. Through another cookie, Acxiom tracks what consumers do on partner Web sites.

At a client engagement we also have utilized a similar strategy (however without using any personally identifiable information like email address) to understand how segments of customers consume content on a website (see image below). This information is used to develop and personalize content on the website and can also inform site layout and design.

What we are witnessing is that companies are trying to bridge the worlds of marketing and web analytics. As we have previously mentioned, third party segmentation schemes which are so pervasive in the marketing world will increasingly become the translation layer between the two worlds.