Kyle McNamara

Writing on the use of data and technology for competitive advantage

Archive for the ‘Information Advantage’ Category

What is Analytics?

Posted by Kyle on June 9, 2008

The other day, I came across a post titled, “The most important thing I know about Analytics is that no-one agrees what it means.” Interestingly enough, my colleagues and I have been working to define the same, and my working definition after a few iterations was:

Analytics is the intelligent use of data and models to discover relationships and causations that companies can use to obtain a competitive advantage by predicting outcomes associated with their processes, customers, and products.

Admittedly, this definition is still fairly broad and doesn’t fully answer my clients’ questions about what it is – or how it is different from all of the Business Intelligence (BI) work they have been doing. In the book, “Competing on Analytics,” the authors describe analytics as a subset of BI, and include a chart that illustrates how analytics can leverage data in the existing BI store to achieve competitive advantage:
Business Intelligence and Analytics

One of the best ways I have found to explain analytics is to describe the types of insights it can offer across the enterprise. Below is a sample of some of the applications I’ve seen of analytics across the value chain.

Applications of Analytics Across the Value Chain

Posted in Information Advantage | 3 Comments »

Fewer Data Sources Improves Analytical Capabilities

Posted by Kyle on May 28, 2008

Creating an analytical capability that allows for rapid model development and analysis requires (among other things) easy access to data with little cleansing. However, in many organizations, customer, product, and transaction data is distributed across systems or databases, making it more difficult to quickly develop a data set for analysis. Last fall, I performed a web analytics project and we were able to use less than half of the data available to us because the data sets came from multiple systems and could not easily be matched. Although we drew some insights from the smaller data set, one of our recommendations was to consolidate the data and make it easier to match so that the next round of analysis could be more complete.

I read today that at RBC, all customer data is owned by the enterprise and held in a central database. I don’t know how Wells Fargo manages its data, but I suspect it is distributed throughout the organization; I have two loans with them, my name is misspelled on one of them, there are different rules that govern how I access and pay each loan – when I interact with them, it’s almost like they are two different companies. If these two banks wanted to target their customers with offers based on product holdings, RBC would quickly be able to understand my customer profile, transaction history, and propensity to buy, whereas Wells Fargo would probably require more time to link data sources to develop the same information. RBC is better able to compete because they already have a central data source for analysis.

Last week, I received a mailer from my gas company pitching air-conditioning options to customers with boilers. (Most homes with boilers lack the ductwork required to install traditional “forced air” systems, but there are other options available.) Granted, my home is over 100 years old and utility companies collect information about services at each premise, but I was still impressed by a utility company’s use of data to develop targeted offers based on what they know about me.

Contrast that with Verizon Wireless. After several years of being a customer, I switched to another carrier – but I wasn’t happy with their service, so I came back to Verizon a few weeks later. Rather than re-activating my old account, they setup a new one, effectively losing the history of our relationship. (When I asked the sales rep if they could reactivate my old account or copy the data to the new one, he responded, “No, we can’t do that,” and proceeded to enter my personal details from my driver’s license.) Verizon can still send me targeted offers, but the extra account history would improve their revenue or churn-prediction models.

Posted in Analytical Capability, Information Advantage | 2 Comments »

Update: More details on coupon use

Posted by Kyle on May 22, 2008

Here are two updates to my earlier post on using online coupons to enhance customer relationships.

First, a WSJ article in which the author looks for online coupons for consumer staples and provides an excellent comparison of several available coupon websites. I found it interesting that the aggregation sites seem confusing to navigate and spotty in their selection, yet there is a $1 off coupon available from the front page of organicvalley.com. She mentions that consumers redeemed only about 1% of coupons issued in 2007. Although this a somewhat respectable response rate to a marketing campaign, it also illustrates that there is plenty of room for improvement in creating targeted coupon mailings to consumers.

Second, an article in PROMO Magazine that reveals that, while coupons are effective for driving sales, 70% of purchase decisions are made in the store, and in-store displays and samples can be just as influential. Anyone who knows me knows I am a huge fan of Costco, and I naturally started thinking about the samples they offer. At noon on a Saturday, the sample army is out in full force, offering tastes of frozen pizza or the newest hummus dip. Many people partake so they can skip lunch or keep the kids happy, but many (including myself) also end up buying the product they’ve tried. While Costco likely analyzes the lift they receive from offering these samples, I wonder how much time they spend understanding the behaviors and preferences of people who visit on different days of the week or times of day – e.g., if I visit on a Tuesday evening, I am probably looking for that night’s dinner and would be interested in entrées, whereas a Saturday noon visit is to stock up for the next few weeks and I would be more open to snack foods and beverages. In addition, why not analyze my past purchase behavior and send me offers for new or complementary products – or offer these products for sampling at times you know I (and consumers who behave like me) will be in the store for sampling, then send me targeted mail letting me know they will be there?

Posted in Customer Life Cycle Management, Customer Segmentation, Information Advantage, Retail | Leave a Comment »

Active Pricing Management to Maintain Competitive Advantage

Posted by Kyle on May 20, 2008

I had lunch the other day with a friend who just got a job as an electronics buyer for a major retailer, and we got to talking about demand forecasting and pricing models. She has some interesting challenges ahead – consumers are more cost-conscious in this slow economy, and even with our stimulus checks, flat panels and Wiis seem like a luxury that many people will forego in order to keep food on the table. I was reminded of a post I’d recently read, encouraging companies to raise prices significantly and frequently. This is not an activity that should be undertaken lightly, and I got to thinking about the intelligence required to do this well.

Rising Costs are Here to Stay

Consumers and businesses are already feeling the impacts of the current economy – disposable income is shrinking due to rising oil and commodity prices, and our summer European vacations have to be scaled back due to a weak dollar. Many of the causes of this – from rising commodity demand in China and India to the Iraq War – aren’t going away anytime soon, so both groups need to adopt a long-term view and adjust their economic decisions accordingly.

Unfortunately, consumer incomes and business profits are not rising as fast as prices, so the proverbial “pie” is shrinking – which means companies’ pricing and positioning decisions have to be smart in order to maximize market share and profits.

Prices Need to Consider Customers and the Competition

Customers are likely to cut back on luxury items, focus on the essentials, and increase their use of discounts and coupons. But not all customer segments act the same, so companies will need understand the needs and preferences of each segment, including the products they value, then determine which segments they want to be in and adopt appropriate strategies. As an example, American Airlines is experimenting with software to identify flights for which passengers will pay extra. This insight will allow them to more aggressively market high-margin flights (products), as well as identify the customers willing to pay the premium. Applying even stronger analytical techniques to this data could help them determine the variables leading to consumer willingness to pay a premium, which they can use to enhance the margins of other products.

Competitors will be increasing their vigilance – looking for ways to keep their share of the shrinking pie and to steal a few bites of the rest. Without actively managing prices while costs are rising, competitors will eat your lunch by adopting smarter pricing strategies. It doesn’t just have to be the customer-facing price that goes up; companies use other techniques such as new package pricing, consignment pricing, fees vs. list prices, reducing product size, and cutting back on promotions. I did some research on using Behavioral Economics to present offers that maximize profits. Actively monitoring the techniques your competitors are using and the success they are having will also provide valuable insights into market activity.

This Capability Needs to be Developed, Institutionalized, and Continually Managed

Amassing this insight about customers, products, competitors, and overall economics is no small task, and there is no off-the-shelf solution that will get you there. Instead, companies should be developing or enhancing their forecasting and pricing models to take advantage of the all of the data available. In a recent article in Consulting Magazine, a senior executive in Accenture’s pricing group mentions that more companies are addressing pricing “not as an initiative but as a capability they want to build.” He goes on to say that they are embedding it into the C-level, as through a chief revenue officer or chief pricing officer. This type of action helps ensure active pricing management becomes a part of a company’s overall strategy and helps maintain a competitive edge.

Elements of an Effective Pricing Strategy

Posted in Behavioral Economics, Customer Segmentation, Information Advantage, Pricing | Leave a Comment »

Profitably Enhance Customer Relationships with Online Coupons

Posted by Kyle on May 19, 2008

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.
Goals of Coupons within each Stage of the Customer Life Cycle

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.

Note: This post has been adapted from my earlier post on Analytical Engine.

Posted in Behavioral Economics, Customer Life Cycle Management, Customer Segmentation, Information Advantage, Pricing, Retail | 2 Comments »

All Reviews are not Created Equal

Posted by Kyle on May 19, 2008

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.

Note: This post has been adapted from my earlier post on Analytical Engine.

Posted in Customer Life Cycle Management, Information Advantage, Retail | Leave a Comment »

 
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