Posted by Kyle on September 9, 2008
Today, I made my first purchase from my mobile phone – a $4 used book through Amazon.com, from a seller I’d never heard of. I was in the backseat of a car, on my way to dinner, kicking myself for not having gone to Borders earlier in the day, and just like that, the order was placed.
Yet I recently read that most consumers are still not willing to use their phones for mobile banking because they are concerned about the security of their data:
According to recent research from Unisys, 71% of all consumers surveyed in 14 countries will not consider using a mobile device to bank or shop online. The issue, for the most part, comes to trusting the technology. The research reveals that more than half of all respondents do not trust their mobile devices to provide a secure transaction and currently only 9% use these devices to conduct transactions involving credit-card payments, money transfers, and deposits.*
At the same time, more money is being invested in mobile banking. These systems are really expected to catch steam by 2010, and over the last few weeks, both Citibank and Bank of America rolled out new mobile offerings.
I recently did some research on applying behavioral economics to improve the success of online offerings, and some of the success factors I’ve seen for online offerings are:
- Limit Available Choices. When consumers are faced with too many choices, they may be overwhelmed and may fail to complete a transaction. You don’t need every aspect of your online offering to be available on a phone, so limit it to key transactions, such as balances, recent activity, transfers, and payments. Citibank doesn’t allow you to add payees to your bill payment account through a mobile device (which is also a security feature). When I use a Wells Fargo ATM, they give me one-touch access to my most common transactions (e.g., withdraw $100, no receipt). Look at your customers’ most frequent transactions, and the ones that they are most likely to want to execute from their phone on the train ride home, and only offer the top few.
- Provide a Familiar Interface. Customers may be dissuaded from completing a transaction if they are uncertain as to how it will be completed. To reduce this uncertainty, you can add cues throughout the process to guide them through – think of the 3-step “Quote. Buy. Print” process offered by Esurance. Services like PayPal and Google Checkout provide a common interface that people are used to – this allows them to store their personal data at a site they trust, and not expose it to an unfamiliar merchant. My $4 book was from a third party I didn’t know, and I was hesitant to give them my payment information; but because they were selling it on Amazon, and the Amazon interface and payment process was familiar to me, I was comfortable completing this transaction.
- Secure the Transaction. One of the most common reasons people don’t shop online is that they are concerned about credit card and identity theft, so businesses need to make sure to provide an adequate level of security. In addition to using familiar services like PayPal and Amazon, transaction-level security must be provided. Citibank’s mobile service uses 128-bit encryption, which is comparable to existing internet service, and Bank of America offers a “$0 Liability Online Banking Guarantee” that ensures customers are not responsible for unauthorized transactions.
- Demonstrate the Benefits. People are naturally resistant to change, so getting customers to use your online offering requires highlight benefits that they will realize in the short-term. Sure, it’s convenient to access your accounts while killing time, but what else is in it for me? Many banks offer SMS alerts when balances run low or strange activity is detected – perhaps that feature must be initialized from a mobile banking session. Wells Fargo waives the cost of online bill pay when you maintain a minimum balance; perhaps banks could also waive it if a customer pays one bill per month from their mobile phone. Or maybe custom offers can be sent to my mobile once I’ve activated the service.
* Source: Consulting Magazine, July/August 2008.
Posted in Behavioral Economics, Retail | Leave a Comment »
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 »
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:
- 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.
- 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.
- 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.
- 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:
- Strong ROI potential. Campaigns that are more effective and lower-cost, targeted at attractive customers, have a stronger potential to deliver a positive ROI.
- 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.
- 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 »
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:
- 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.
- 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.
- 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.
- 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:
- 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.
- 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.
- 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.
- 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.
- 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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