"Data" seems to be the word that guides any marketing decision in recent years. But what data are we talking about? What information is most valuable to your company=
Cascades of green cinematographic code aside, the truth is that technology has truly accelerated this process of collecting and reading information, in such a way that it can predict and anticipate customer’s needs.
Taking advantage of the advent of technology and data analytics is the best way to increase profitability, build loyalty relationships with the market and consequently increase retention rates. By crossing information at different points of contact with the consumer, highly personalized and segmented experiences are created.
Predictive analytics is rapidly advancing to the podium of e-commerce solutions. It not only enhances Share of Wallet, but also delivers greater added value to the customer, who finds in the brand a reference for future purchases.
Machine learning algorithms behind Next Best Offer (NBO) are among the most innovative predictive analytics solutions, which help marketers and organizations deeply understand customer’s consumption habits, consequently guiding the plans to better serve them.
Next Best Offer as the next best solution
The historical relationship with retail implies an almost personal relationship between sellers and customers. There was such a committed relationship that some customers wanted to buy from the seller, rather than to a brand itself.
The challenge is precisely to transfer this level of personal relationship to the digital context and we have never been around anymore with the help of solutions based on data analysis. That’s why the Next Best Offer artificial intelligence solutions guarantee a highly personalized offer, at the moment, price and most convenient channel.
In the case of E-Goi, the NBOs are based on collaborative filter models whose central object is the customer itself, starting from the premise of “observing” their purchasing behavior and establishing relationships between customers, according to similar behaviors.
As a practical example, let’s consider clothing stores. In addition to integrating all data in a CDP (Customer Data Platform), coming from different devices and environments, the NBO algorithm is able to map the frequency and consumption habits of customers, achieving similar relationships between customers. When checking common products among several customers, the product recommendation algorithm will recommend complementary products, which are not purchased by one or more customers:
Steps to implement an NBO model:
- Define goals
If there is something wonderful or anguishing as managers, we are always asking questions (how many answers we look for, more questions arise!). When considering implementing an NBO model, it is important to ask:
- Do you want to attract new customers?
- Do you want to increase sales?
- Do you want to reduce the retention rate?
- ¿Increase the “cuota de cartera”?
These questions must define clear goals, however, one must be alert to the flexibility of adapting it, thanks to the depth of the analyzed data.
- Collecting Data
Now it’s time to use the diving equipment. We do not need to hold breath, but we should definitely undo some preconceived ideas about the market and consumers. A rather funny case is Walmart supermarket chain. When analyzing consumer habits in the store, using the Wi-Fi monitoring, a curious correlation emerged: the same consumers who bought beer also bought diapers. No, Walmart did not sell alcohol to minors, but rather to their parents. This totally disruptive information meant that Walmart reorganized the presentation of its products, supernaturally boosting its upselling strategy.
That’s why this immersion in data should really be done, with the proper equipment. As consultants for E-goi Digital Solutions, we suggest the integration of a CDP so that the possibility of collecting data is as complete as possible. As we have commented on in our blog, a customer data platform can compile data on store, on website, from CRM and present all this information in processable and highly effective knowledge for your commercial purpose. The possibility of collecting SoMoLo data (social, mobile and local) only improves the accuracy of the collected data.
- Analyze and implement an NBO model
The challenge is to see through the numbers. Transform information into viable strategies through analytical processes. This process can include segmentation, data modeling and adaptation to business rules. Even the ability to offer different commercial proposals in different channels depends on the potential conversion for each customer.
Following the idea of clothing stores, let us take as an example a segment of customers that only buys from the store, with an average purchase ticket of €100. Is it worth it to offer a 10 € discount bonus on the online store? (It’s not an unreasonable question, it depends on the question that you ask in step 1) Is it better to bet on coupons sent by SMS?
On the other hand, if you have another segment that buys mostly through the online store, why not sending an e-mail with recommended products, through collaborative filter model? An example of success in the Ali Express or Amazon marketplace, whose strategy is to send e-mails with recommended products a few days after the last purchase.
That’s why it’s important to …
- Learn and grow
It is a very important motto to keep in mind! If your goal is to increase your share of wallet, it is important to focus on what really matters: customers. As much as it makes sense to follow a recommended line of products that makes sense for us as commercial managers, the truth is that the data gives the final judgment.
As digital consultants and being in contact with very heterogeneous commercial realities, knowing that the depth and precision of the figures has been an ally in the overcoming of the strategic objectives of our clients.
The mindset of testing, learning and evolving has allowed us to refine strategies, channels and information to deliver to the market.
In conclusion…
E-goi’s NBO model automatically qualifies and recommends products that are most likely to satisfy a customer. This type of strategy optimizes the conversion rates and coordinates the delivery of the best offer at an attractive price and the most adequate channel.
With actions and personalized offers, relevant and in real time, the possible results of the implementation of this technology are a challenge. In addition to mapping real data, collected in store and with real consumers, an NBO strategy impacts different organizations at a strategic, commercial and operational level.
This strategy is able to break the ice with the market and offer the best possible shopping experience.