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Data Mining / Cross-Selling






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Data Mining / Cross-Selling Data Mining / Cross-Selling Document Transcript

  • Cross-SellingCross selling is a strategy that pushes new products to current customers based ontheir past purchases. It is designed to widen the customers loyalty on the firm anddecrease the probability of the customer switching to a competitor.There are someelements that might influence the definition might include: the size of business,theindustry sector it operates within and the financial motivations of those required todefine the term.The obvious metric that most people think of when evaluatingcross-sale promotion effectiveness is conversion rate.Conversion rate identifies thepercentage of people who, when buying the “original” product, choose to alsopurchase the “promoted” product.Making changes that raise conversion rate increase sales of the promotedproduct.However, this conversion ratio is more a reflection of the relevance of thepromoted product to the original product than it is a measure of profitability.Thecore goal is to maximize profitability, while providing additional value to customers(who are better off or more satisfied with the original purchase when they alsopurchase the promoted item). Introducing a cross-sale promotion can increase,decrease, or have no effect on the rate of purchase (conversion percentage) of theoriginal product.Business owners need to measure the original-product conversionrate for their customers who were shown the cross-selling promotion versus thosethat were not. Second, you’ll want to know what the ideal products to promote are.Typically, more than one cross-sale promotion is presented to a customer at a time.For this example, assume 3 promotions are displayed.Let assume that a data-mining exercise has identified 5 products that could bepromoted as cross-selling items for this “original” product. Which three of the fivecan be selected? The three that is expected to be the most profitable should beselected “Most profitable” can be calculated as (profit per promoted item) x(conversion % – of the promoted item in the context of the original item).
  • If there is no conversion percentage data, it can be gathered (through testing) orpredicted (through modeling). There are many aproaches for predicting the degreeof similarity, or implied relevance, of one product to another – but all of them aretoo detailed to cover in a blog article. Some companies also report average ordervalue (AOV), but that’s not necessarily an indicator of profitability. It may be acomponent of profitability, but not necessarily.There is one of real life businessexample which is in fast food industry.Customers are often invited to try newproducts or established complimentary items.For example, when an individualorders a hamburger at a local fast food restaurant, the server will often ask thecustomer if her or she would like a side item to go with the hamburger. If therestaurant is offering a new dessert, the server may also suggest to the customerthat the new item may be a desirable compliment to the hamburger. By employingthis simple approach, the server may entice the customer into making anotherpurchase above and beyond the one originally intended.There is one more example about cross-selling,and its in retaling sector. Whenpurchasing a washing machine, the salesperson may extend some type of specialoffer to the customer in order to entice the buyer into also purchasing a dryer.Managers who are responsible with their own products marketing should not thinkthat we are irritating them with too many sales pitches to buy more , but theyconsider the some ideas to improve opportunities for cross-selling.One of them is that let nature take its course.Many cross-selling opportunities arisenaturally. If you are selling tennis racquets, for example, you can also offer a bag,balls, lessons and accessories. To gain the extra sale, you might simply have tomention that the other products or services are available.The other idea is "Stay Relevant".If you overload customers with too many unrelatedcross-selling suggestions, you may blow it. Offering socks with shoes is certainly agood fit. But if your attempts to cross-sell are not closely related to the originalpurchase, they are far less likely to succeed.
  • Moreover, "Post expert recommandations" is the another idea.One way to facilitatecross-selling and up-selling success is to state specific recommendations fromprofessionals, experts or other customers. This could be a chef’s recommendationon a menu, a doctor’s recommendation on a mailer, or lists of related items thatother customers have purchased on a website. When you buy a book atAmazon.com, for example, the site automatically lists other books purchased bypeople who bought the same book you just ordered.The fourth idea is "Trainemployees in cross-selling techniques". The approach must be built around servingthe customer, not just selling more stuff. For example, you might describe how theadditional products or services would complement the original purchase andfurther solve the customer’s problem.The other important idea that time is important.ss-selling and up-selling can occurat different times, depending on the products and services you are selling. In somecases, the best time is while a customer is trying something out. If they are lookingat a low priced digital camera, for example, but seem disappointed in a lack offeatures or performance, they may really want a higher priced model. Or you couldsuggest a belt to go with a pair of pants while the customer is trying them on. Otheritems are more appropriately offered once the initial buying decision has beenmade, such as an extended warranty.Furthermore, "leverage the cross-selling potential of your website" is an othercrucial idea for cross-selling. Position cross-sell and up-sell items throughout yoursite in places where they can help educate shoppers on the depth and variety ofwhat your business offers. Try mixing and matching different items to see whatworks best.One of the important idea in order to improve the opportunity for cross-selling is "offer a range of prices". If you suggest three items to complement aproduct, try to offer a mix of price points. The lowest cost items are most likely to bepicked up as impulse buys. But other items that meet the customer’s needs can alsosell at higher levels.
  • The last important idea is to try product or service bundles. Bundling has long beenused as a way to entice shoppers to buy not just a single item, but an entire groupof items that go together. Offering a price break on package deals will help closethe sale.Moreover,there is one thing important which is need to be together with theseeight ideas is the recognizing the customers well. I found some short discourseabout cross-selling and up-selling which were written by Okan Özdemir, AgentsSales Group Manager of Allianz and he has a motto about it like "Cross saleincreases the loyalty of the customer "."Customers should be described well for cross-selling"If one insurance agent keeps many policies which are belong to one insuree,theinsurance agent becomes a consultant for this insuree so that he may trend to havethe familiar agent make his own transactions. Also,we are strongly recommendingthat the insurance agents should consider the customers datas,purchasingbehaviours and overall tendency in order to increase the agents sellingpotential.Moreover,CRM system must work actively in the background so that thesegoals which were mentioned can be fulfilled.On the other hand, there is onenegative fact about the people who do not cross sell or up sell. The one reasonwhich they dont use these techniques because of. The point is the "FEAR !". Fear oflosing is the number one reason salespeople dont cross sell or up sell moreoften.Here are the some big reasons why sales professionals continue to resistcross-selling and up-selling ;
  • For FEAR;"Will I risk the order I just received if I ask for more ?"For TRUST;"Im not going to confuse this account by pitching a bunch of new products."For DELAY;"Ill ask for a referral later-once we are in a better position with this account."For CREDIBILITY;"This isnt my area of expertise." "10 Cross-selling Techniques That Work" • Be RelevantOffer suggestions which are related to the original sale, but do not attempt to sellyour customer the entire store. If a customer buys some peanut butter, show themthe jelly rather than the green beans. Your customer does not want to be inundatedwith too many choices when they have selected an item. • Maximize positioning on your sitePlace your cross-sell items where they will be seen near related items on your site.Use a suggestion tool which is offered with a lot of eCommerce software. Let yourcustomers see the accessories that you have available, but again, keep the selectionlimited to a few.
  • • Bundle your productsOffer discounts for buying items in packages. Combo meals have worked extremelywell for the fast food industry. Your customers thrive on the thought of makingdeals, so they will spend ten extra dollars just to save five on a pair of products. Thatbehavior increases your bottom line. • Offer several price pointsWhen you are giving your customers a choice, present products from three differentprice points. You want them to know about the latest and greatest items, but youalso want to give that customer some practical choices about what to buy. Do notbe completely focused on cross-selling or up-selling the most expensive items. • Be observantThis suggestion applies more to the brick and mortar cross-sellers. Watch yourcustomers and understand the signals that they give you. If you can tell thatsomeone is not happy with the features of your skis, give them an option of buyingthe skis that are higher on the price chain. Timing is everything, and the astutesalesman can tell when their customer is making their decision. • Offer free shippingFree shipping is a great way to get your customers excited about an order. Set aprice threshold that your customers must pass to qualify for that bonus, and theywill do quite a lot to reach it. If a customer is a few dollars away from the mark, theywill add another item to reach your arbitrarily determined price point.
  • • Create some urgencyUse the idea that your items are available at this price for only a limited time. Giveyour customers some urgency by telling them that there are only a few of theirfavorite items left on the shelves. Keep the deals short so you can use phrases like‘ends Thursday’ in your advertising. • Show your top rated suggestionsSet up a system of relationship selling. Let your customers know what othercustomers have bought and what products they believe are the best. Give yourcustomer the ability to compare feature to feature on each of the products that youhave, rather than striking all over the board. Your customers love to receiveinformation. • Keep ad-ons on check outCustomers do not want to repeat the process of checking out when they have anadd-on item. They do not want their impulse buy to throw them to the back of theline. They do not want to re-input their credit card information just because theywanted to buy a pair of socks with the shoes that they were purchasing. • Make the experience personalYour customers are the most important individuals in the world. They are the oneswho keep the doors open, and they want you to cater to their needs. Focus theattention on them when you are cross-selling by using phrases like, ‘you might alsolike’ or ‘do you want’ rather than canned corporate phrases that start with ‘wesuggest.’
  • Data Mining – Cross Selling"What is Data Mining ?"Data mining is the process of extracting patterns from data.Data mining is seen asan increasingly important tool by modern business to transform data into businessintelligence giving an informational advantage. It is currently used in a wide rangeof profilling practises, such as marketing, surveillance, fraud detection, and scientificdiscovery."Data Mining in Business"Modern organizations are overwhelmed with data, producing mountains of textualdocuments, spreadsheets,and web pages. Every activity of the organization yieldsmore documents with each passing day.But a surprisingly large volume of dataremains relatively inaccessible. Any organization is likely to have data that remaintantalizingly out of reach, either through a loss of institutional memory or throughneedle-in-a-haystack search complexity.The idea that information resources shouldbe counted as an asset, and managed to yield maximum advantage emerges incycles every few years. The concept has most recently manifested itself as part ofthe “knowledge management” approach. However the idea is packaged, there is agrowing acknowledgement that information resources are important, and that theyshould be consciously cultivated.
  • "Data Mining Models for Cross Selling"If your company offers multiple products or services, you can use data miningmodels to find prospects for cross selling: • Based on the products customers have bought in the past, data mining models can help you determine which products they are most likely to buy next. • In addition to the information that you have about your relationships with customers, you can leverage demographics to see which customer segments are most likely to add more products or services to the bundles they currently purchase. • With the results of these data mining models, your company can allocate resources toward your more valuable customers and prospects.If you record information about your customers in a database, you have theinformation necessary to deploy many data mining models. At The ModelingAgency, we use the latest models to provide our clients with insights to improvetheir businesses. We also offer a range of seminars so that you can learn toimplement the same data mining methods in-house.
  • Success Story:Dexia BankDexia Bank is part of the Dexia Group, which operates in Belgium, Luxembourg,France, Germany, Austria, Spain, the United Kingdom, Portugal, Switzerland,Sweden, Italy and the United States. Dexia Group’s three main activities cover threeareas: the funding of public utilities and financial services to the public sector andmajor corporate customers, general commercial banking services, and privatebanking and administrative and financial wealth management. In a recent datamining project, Dexia Bank set out to determine what products its customers arelikely to purchase.BenchmarkingThe objective of Dexia Bank’s first large-scale data mining project, which ranbetween March and September 2001, was to select 5 target products from thebank’s range of around 100 bank products and search for the most appropriatetarget public for each of these 5 products among the bank’s existing customers.Thus the project had to produce a list of products and a score per customer to rankthe likelihood of each specific product being bought by that target public.Thecorporate data warehouse provided the information in three categories: personaldata, whether each customer had already bought any of those specific products,and business indicators for each product, such as characteristic values (e.g. for amortgage loan, this would include the term, monthly repayment, etc).
  • While these data from the data warehouse were verified and selected, they were stillraw data and not mineable, i.e. Not readily interpretable by business people.So whydid Dexia Bank choose SAS for its data mining? “The choice was not a foregoneconclusion,” said Geert Van den Berge, Data Miner and project leader at Dexia Bank.“Dexia Bank had already built up a certain amount of experience with SAS by usingit for other purposes, so that was definitely an advantage,” he conceded. “We werealso aware that SAS Enterprise Miner was being used very successfully in otherbanks and that they were very satisfied with it, which was another strong argument,but we still wanted to make our choice based on the results of exhaustivebenchmarking.One of our top criteria was the potential for communicating results to all the partiesconcerned in a uniform way, so that we can be certain that everyone is working withone version of the truth. A second criterion was assessment, i.e. the ability tocompare the performance of different models in a single graphic. By assessing theresults gained from each stage of the process, you can determine how to modelnew questions raised by the previous results, and thus return to the explorationphase for additional refinement of the data. Moreover, the SEMMA data miningmethodology used by SAS is the most natural approach, and the SAS data miningsolution addresses the entire data mining process."SEMMA data mining methodologySAS Enterprise Miners "Sample,Explore, Modify, Model, Assess" (SEMMA) approachprovides users with a logical, organized framework for conducting data mining.Beginning with a statistically representative sample of the data, this methodologymakes it easy to apply exploratory statistical and visualization techniques, select andtransform the most significant predictive variables, model the variables to predictoutcomes, and confirm a models accuracy.
  • "However, we reversed the first two stages," said Geert Van den Berge. "We startedby exploring the population first, then did the sampling afterwards, since theexploration of the population in terms of useful parameters first had to give us abetter insight into the population studied." Geert Van den Berge also underlined thepower of SAS data mining tools and the teamwork necessary to conduct such aproject successfully. "SAS offered us so many powerful functionalities that non-specialist users were able to draw false conclusions.Although the point-and-click graphical user interface of SAS Enterprise Miner is veryuserfriendly, a certain amount of statistical and data mining knowledge remainsessential. There is also a definite need for teamwork throughout the entire project -with marketing people for the business knowledge, with people who are veryfamiliar with the contents ofthe corporate data warehouse, and with hardware specialists, because a project likeours needs large amounts of disk space and processing power."Lessons learnedThe sample needed to construct predictive models comprised 370,000 people, 75%of whom were used for modeling, and 25% for a business test. Why this split? "Thebusiness people insideDexia Bank were a little skeptical about our approach, so theywanted to verify our results via a business test," replied Geert Van den Berge.Association rules were used to select the 5 target products, i.e. you could say that acustomer already using product X, Y and Z was likely to be interested in a specifictarget product. Mineable data were obtained by calculating useful businessindicators per customer and per product. Assessment, scoring, and finallyextrapolation followed the construction of predictive models to the 2.8 million usergroup through iteration.
  • Apart from the need for close cooperation between all the parties involved, GeertVan den Berge concludes that other lessons were learned with this first data miningproject at Dexia Bank. “You must have a precise, carefullyevaluated definition of theexpected deliverables, good data preparation is essential to enable experimentationin SAS Enterprise Miner, the hardware configuration must be fine-tuned, and theresults must be interpreted in a uniform way by all the parties involved. "Asuccessful data mining project such as this is an excellent way to realize a highreturn on our data warehouse investment," concluded Geert Van den Berge.The SolutionUsing first a sample, followed by the complete corporate data warehousecontaining monthly refreshed data on 2.8 million customers, Dexia Bank set up adata mining project with SAS Enterprise Miner. Raw data were transformed intomineable data, a scoring model was developed and, finally, a customer’s degree ofmaturity to buy a target product was determined.