As the US and the world economies encounter a downturn and firms look to scale back, Marketing is often one of the first places to face budget cuts.  Forrester reports that many companies expect to cut their marketing budgets by 3%.  But how do you maintain or grow your customer base and revenues when consumers are spending less and your message isn’t getting into the marketplace as loudly?

We think the use of online coupons deserves a harder look.  Emailing your customers and prospects with newsletters, product updates, and coupons is certainly nothing new, but it’s now well-positioned for even greater success:

• Companies are getting good at it. After dabbling in techniques like SEM and direct email, firms have gotten better at driving profitable growth from these methods, and many are increasing their focus on online advertising as a cheaper way to spend their marketing dollars. 
• Consumers want more of it. During these uncertain times, consumers plan to increase their use of coupons to save some money.  Sending these options straight to their inbox or mobile phone accomplishes that goal and positions you as a preferred provider.
• Consumers who use it are attractive prospects. Compared with consumers who only use offline coupons, Forrester reports that users of online coupon tend to have higher incomes, shop online, like to try new products, and influence peers.  Younger consumers also use coupons, and they can be a good avenue to get the word out about your product.
• More data is available to help you win at it. More firms sell marketing lists (or can help you run campaigns to get new lists), segmentation data helps you understand consumers’ preferences and desires, and syndicated data helps you understand purchase behavior.  Combining this data gives you incredible insight into consumers to tailor unique marketing messages.

You don’t just want to throw promotion dollars at existing customers to give them discounts on things they were already going to buy; rather, you likely want to use those dollars to deliver positive returns and achieve business goals – such as acquiring new customers, increasing market share, or increasing wallet share.  Doing this requires targeting offers to customers based on their stage of the customer life cycle:

1. Acquire. Coupons can be a good tool to help consumers overcome the risk associated with trying a new product; if a new product is cheaper than the one they normally use, the savings might be worth trying.  You can use them to attract entirely new customers to your firm, or to get your existing customers to try a new product line.  Targeting early adopters can also help generate buzz, as they will influence friends and family to buy the product as well.
2. Grow/Stimulate. Once you’ve acquired a customer, you want them to maintain or increase their purchases.  Two ways of stimulating usage are encouraging them to try a different variety (e.g., color, size, flavor) or showing them new uses for the same product (e.g., using Q-tips for craft projects in addition to hygiene).  In this stage, the focus should be on the marketing message, the coupon being used to help seal the deal and drive the customer to the store.
3. Manage. In this stage, your customers are steady-state users, and couponing may not be required to retain them. However, these consumers present a good opportunity to test new offers on an already loyal customer base and measure the response before using them on the general public.  You might test them using different demographics, layout, or wording, perhaps even running controlled experiments to determine which of two offers is more effective.  We’ve done some research on the use of Behavioral Economics to improve offer design, which might be helpful in performing this testing.
4. Reclaim. If customers reduce their consumption or begin to try competitors’ products, you can use targeted offers to reintroduce your product and retain them as customers.  However, depending on their needs and your product pipeline, you may otherwise opt to move back to the beginning of the life cycle and acquire them as customers of another of your products.

This strategy requires a high level of customer insight to understand preferences and stages in the life cycle.  You can gain this insight by applying segmentation schemes to your lists of customers and prospects, and by analyzing your customers’ history of purchases and coupon redemption.  Applying a rigorous testing approach will help you identify the most effective offers for each customer and stage.

Applying this framework to understanding your customers and targeting coupons will deliver several benefits, including:

1. Strong ROI potential.  Campaigns that are more effective and lower-cost, targeted at attractive customers, have a stronger potential to deliver a positive ROI.
2. Better data to analyze results. Results of online campaigns are easier to track and measure than traditional campaigns, particularly if your coupons lead customers to purchase from your own website.  Analyzing results from campaigns that involve multiple partners may require a different approach, as Vishal outlined in his earlier post on trade promotions.
3. Better customer relationships. You can use the insight you’ve gained about your customers’ behaviors, preferences, and purchase history to continually develop targeted offers.  This level of personalization will help you deliver the right offers to the right customers at the right time, and ensure that your promotion dollars are spent most effectively.

Trade promotions represent a significant part of the cost of consumer packaged goods (CPG), and an important part of a go-to-market strategy, it is the second highest expenditure after COGS and represents two thirds of marketing spend. Yet, there is little visibility into where this spending actually goes, or how effectively it increases revenues, expands market share, or creates brand awareness among consumers. The irony of the system stands affirmed with the fact that extra pressure generated over the supply chain for immediate deliveries is borne by the end consumers which has pushed the actual monetary benefits reaching the end consumers down to 50% over the years.

Trade promotions have taken various forms and attained different levels of complexities as the markets and available technology have matured over time:

1. Slotting allowance: reward paid to a retailer for the retailer’s shelf space
2. Case allowances: reward offered to a retailer when merchandise is purchased by the case; greater the number of cases, greater the discount
3. Account specific promotions: reward for specific big/small accounts (retailers)
4. MDF or market development fund: a lump-sum payment made once or twice per year which the retailer may (or may not) spend on marketing the product
5. Scan back promotions: reward for higher sales, where the retailers are required to submit paperwork as a proof of the sales, to qualify for the trade promotion allowance

Data at the most granular level is required for effective management of trade promotions, but most of the time it is not available or is highly fragmented and inconsistent (at times the most granular level data might be available in segregated laptops !). Combined with the differences in geography and final pricing at the retailer level, firms are most of times unable to break down trade promotions in sub-processes. Given the level of complexities involved, the market essentially lacks standard meaningful metrics to rate a promotion at an absolute or even at a relative level within the firm (*more than 70% of the trade promotions are not evaluated properly and less than 30 % are considered profitable). Eventually, many firms end up taking the outcomes of the promotions rather than the whole process as a proxy for the performance metrics, thereby making continuous improvement a challenge if not impossible.

One way we have approached the analysis of Trade Promotions is performing the following steps:

1. KPI Identification: Identifying the key parameters involved within the system will crucially determine the effectiveness of the whole procedure. Some common metrics include:
• Time periods i.e. the average active period of a promotion and the time difference that may be involved to measure its effect.
• The expected increase in the sales volume.
• Consumer growth.
• Discounts reaching the customer or customer pass-through.
• Increase in the shelf space.
• Increase in revenue/profit/profit margin for every dollar spent in trade promotion
• Increase in the no. of cases sold per dollar spent in trade promotion
• Increase in brand value over the specific demographics
2. Data Gathering and Quality Audits: Gathering the data from sources that may include invoice history, third party/syndicated data (e.g. IRI/Nielsen), retailer level sales data or POS data, and even individual laptops.
3. Data Preparation: Involving processes like missing value treatment, data anomaly treatment and creation of the final data set with maximum level of detail possible with respect to the KPIs.
4. Analytical Modeling: Applying analytical techniques to connect these disparate data sources to quantify KPIs
• Linear Regression at the retailer-product level to attain coefficients (symbolizing the dependence of sales over the KPIs) of attributes for the time duration of specific promotions (which may be around 2-6 weeks).
• Predicting the future values of the coefficients and product-sales using a beta algorithm based on Markov Chains and Monte Carlo simulation, developed by Diamond for this purpose.
5. Recommendation Developments: findings from the data modeling to determine most profitable promotions -e.g.
• Retailer-product combination
• Geography
• Specific time of the year etc.

Although the lack of a centralized TPM system within CPG firms will be the biggest obstacle for advanced analytical analysis of the same, it’s still possible to gain valuable insights for better implementation and to improve the process on a whole.

*Sources: ACNielsen Survey of Trade Promotion Practices (2005), Cannondale Assoc’s, DCI Assessment

A few weeks ago, Shantanu wrote on recommendation engines and how user feedback and ratings can be a part of recommendations you provide to your customers.  But if you have ever looked through user recommendations while shopping online for a product, stock, or movie, you know that they aren’t all helpful.  Ideally, user ratings would accurately represent the population, but not all feedback is created equal, and there are some inherent challenges in these systems:

1. Not everyone will rate.  People may read the ratings when shopping for an item, but they won’t always come back to rate.  Unless a site offers an incentive for rating a product, a customer’s only real incentive for doing so is to talk about how much they love or hate it; moderates may be under-represented.
2. Ratings will be biased.  People’s individual biases produce variances in ratings, even if strict guidelines (think about employee performance reviews) are presented.  In addition, new raters tend to rate high.  Their average ratings decrease over time as they rate more items, presumably because they are exposed to more items and have a better sense of an item’s value relative to alternatives.
3. Ratings are averaged, masking the underlying data.  Because people often only rate items they feel strongly about (love it or hate it), and an average of those extreme ratings may not truly represent the actual sentiment surrounding a product.  For example, a review of rankings on Amazon.com revealed that “the reviews for the majority of the products have an asymmetric bimodal distribution. For these products, the mean of the online product reviews does not necessarily reveal the product’s true quality, resulting in misleading conclusions about the product’s future success.”  In addition, established products are at a disadvantage against new ones.  Consider a product that has received five rankings, four “5s” and one “4,” giving it an average rating of 4.8. If a new product enters the space and receives one “5” rating, it will be ranked higher than the other simply because it has fewer ratings. 
4. Ratings may be false.  Take the case of the Whole Foods CEO who posted disparaging comments about a competitor on a message board while talking up his own company, later stating “Sometimes I simply played ‘devil’s advocate’ for the sheer fun of arguing.”  Other visitors may post false comments to artificially affect the rating, and these are not always removed from the calculation.

As more products are marketed and sold online, feedback-based ranking systems are increasingly common components.  Amazon and eBay were among the first to use visitor ratings to rank products and sellers, and in February, Yahoo launched its Buzz service, which asks readers to click on their favorite stories, then uses those ratings to determine the most popular articles on the web.

Why is this important?  Because accurate product ratings help predict that product’s success, and higher product ratings lead to more sales – or, as Allen & Appelcline state, “the value of individual items (most frequently goods) rise or fall based upon the largely subjective judgment of individual users.”  So what can you do to ensure the rating system on your own website accurately reflects your customers’ views and the value of your product?

We’ve seen some thought around using analytical techniques and Bayesian mathematics to create better product rankings.  Some of the solutions explored include:

1. Adjusting ratings based on known biases.  Since some people rate higher or lower than others, one approach is to assign users a “User Optimism” value based on their rating history, and adjust the product’s overall rating based on the raters’ optimism value.  Another approach is to remove all ratings from people who have only rated 1 or 2 items, helping to eliminate new-rater optimism or “drive-by” fraud.
2. Weighting a product’s ratings based on the number of ratings received.  When a product has a small number of ratings, these ratings should count less than those for a product rated many times.  To achieve this, you can add a “magic value” into the algorithm that calculates a product’s average ratings.  This “magic value” brings products with few ratings closer to the average ratings of all products, then reduces its effect as more ratings are received, allowing established products’ ratings to float freely and more closely reflect the average of its ratings.
3. Adjusting the algorithm to account for bimodal distributions.  To account for products that only have ratings at the extremes, one approach is to use a dual-point estimation model to more accurately reflect customers’ views of the product and predict the product’s success.
4. Analyzing a customer’s rating history for fraud.  Someone who rates several products on your site should have a predictable rating pattern.  You can analyze individual customers’ accounts to identify and remove anomalies that might skew product ratings.
5. Encouraging customers to leave text-based feedback.  When a person takes the time to write out a product review, and knows that her name will be attached to it, she generally does a better job in her rating.  These reviews tend to be closer to the average than those without.

Although your customers’ product ratings may be imperfect, they can still yield insights and value with the application of a good set of analytical techniques.

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).

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, s