[China Glass Network] In the past, when customers shop in the store, they would rely on familiar salesmen and sometimes shopkeepers in neighborhood shops to help them choose their products. These shop assistants or shopkeepers know the old customers and can quickly find out what customers want, and often recommend them to buy other items that they have never thought of. This kind of humane shopping atmosphere is now less and less common. There are a shortage of first-line sales staff in many retail locations, and there is limited knowledge. Online shopping is mainly based on consumers themselves.
With advances in information technology, data collection, and analytics, companies can get increasingly refined data from demographics to consumer sentiment to consumer online clicks, and provide customers with similar or even superior information based on this information. In the recommended shopping recommendations of the owner, at the right time, at the right price, through the appropriate channels, guide customers to buy "appropriate" goods or services. The authors refer to these suggestions as "nextbestoffer (NBO)." Developing a complete NBO involves the following four steps.
1. Determining the goal Many companies have invested in NBO operations, not because of lack of analytical capabilities, but because of the lack of clear objectives. So the first question is: What do you want to achieve? Increase revenue? Increase customer loyalty? Expand wallet share? Or attract new customers? You are always ready to correct your goals based on changing circumstances.
2. Collecting data Next, you need to collect and integrate detailed data about customers, products and shopping backgrounds. In addition to mastering some basic customer information such as age, gender, number of children, address, income or assets, spending mentality, lifestyle, etc., you can also track consumer historical purchase records and social localized mobile information (SoLoMo). In addition, you must have a system that categorizes product features in detail, which helps you determine which products are more attractive to consumers. Later, you must also consider the following factors: the customer's access to business channels (face-to-face, phone, email, network), the reasons for contact and the environment, and even reflect the volume and tone of the customer's mood is calm or sad. In some cases, the background factor of the purchase may also include the weather, which time of day or day of the week, and whether the customer is alone or a companion, and so on.
3. Analysis and Implementation Using statistical analysis, predictive models, and other tools, first generate a large number of potentially valid shopping suggestions, then select recommendations based on business rules and determine appropriate delivery channels for each recommendation. In general, the channel of contact with customers is the appropriate channel for submitting NBO. For example, customers of CVS chain pharmacies can get customized coupons as soon as they brush the Extra Care loyalty card on the service terminal in the store.
Companies can test purchase suggestions through multiple channels to identify more effective channels. At CVS chain pharmacies, the ExtraCare card's shopping advice is delivered not only through the service terminal, but also through cashier, email and targeted flyers, and more recently through coupons sent directly to the customer's mobile phone.
Shopping advice should be moderate, and companies need to carefully consider the timing of recommendations, and monitor customer contact frequency to avoid excessive recommendation and cause customer dislike.
4. Learning and Development Companies must measure the performance of each shopping proposal, learn from lessons learned, and draw lessons from it to guide the design of future shopping recommendations until the new data shows that these rules need to be revised.
It is quite difficult for any company to include every possible customer, product, and background variable in the NBO model, but no retailer can collect basic data such as demographics, consumer sentiment, and historical purchase records. As the amount of data that can be collected and the number of interactive channels increase, companies that cannot quickly improve their shopping advice will be far removed by their opponents.
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