In an increasingly anxious context, a wind of panic is blowing over the activity of retailers, who are facing the possibility of closing their points of sale. Although a certain number of consumers naturally turn to e-commerce, many of them are accustomed only to making purchases in stores, and the brands that are still able to maintain their e-commerce activity are deploying drastic measures to bring these consumers to their websites. Faced with the urgency of the situation, the temptation is indeed great for retailers to intensify the number of emails they send out and the use of aggressive promotional campaigns to try to attract the maximum number of consumers to their stores.
The risk is therefore a resurgence of polluting, senseless and intrusive mass marketing, to which consumers no longer react and which further deteriorates retailers' margins. For retailers, even more spending on futile attempts to maintain their business.
The whole challenge today for marketing teams is to encourage their consumers to be omnicentric in order to counter this lack of traffic, but to do so with reason and relevance.
Developing the omnicanality of their customers also represents, today, a real opportunity for value creation, since an omnicanal customer spends on average 3 to 4 times more than a customer who only buys in-store.
To meet this challenge, marketers can rely on the support of artificial intelligence, which allows them to easily draw on the vast amount of customer data at their disposal. Whether it is cold data, coming from CRM, or hot data, coming from the navigation of people on site, their exploitation via predictive models, allows to use the right levers to solicit each individual in a personalized way, with the right messages at the right time, to generate qualified traffic and online conversions.
Brands can activate 2 levers :
- Quickly generate qualified traffic on your website
- Detect and transform the purchase intentions of on-site visitors
Quickly generate qualified traffic on your website
Bring existing customers to your site with meaningful messages
In order to create qualified traffic from CRM actions, it is first necessary to have a solid understanding of your customers, and the use of predictive marketing models allows you to identify the right pretenses to solicit each of them individually.
For example, properly identifying customers' motivations for buying helps to establish strong reasons for approaching them. AI models make it possible to segment different customer profiles in terms of their propensity for promotion, as opposed to product novelty.
Based on these insights, the marketer will activate individualized campaigns according to these profiles, offering "sales addicts" an exceptional discount on online purchases and pushing the products from the new collection to "early adopters", without highlighting special offers, but rather advantages such as free delivery and return.
The relevancy of the associated product recommendations is also paramount.
Using these recommendation templates, AI analyzes each customer's transactional history to identify the products that are most likely to interest them among those available in stock.
It is in this combination of refined targeting and personalized recommendation that the key to successful campaigns lies, which will help to develop the omnicanality of individuals who have until now remained customers who only buy in-store.
Find customers with the same profile as your most loyal customers
Through the implementation of predictive segmentation and qualification models, artificial intelligence makes it now possible to extend well beyond "classic" segmentation and to build up detailed and operational customer knowledge.
By tapping into this real "customer DNA", it is now possible to build fine-tuned audiences, dedicated to the recruitment of high-potential customers. Using look-alike, for example, retailers target "twins" of their e-commerce and multi-channel customers on Facebook, Instagram or search engines, to make sure they are bringing the right individuals to their site.
Detect and transform the purchase intentions of on-site visitors
Once provisions have been made to ensure the generation of qualified traffic on the site, brands must be ready to turn these visitors into buyers with the best strategies.
Using a scoring model, machine learning algorithms are able to detect the purchasing intentions of individuals who visit the company's website and also identify the products that are at the origin of these intentions.
By reconciling offline and online data, which enables the linking of an individual's browsing behavior with their CRM data, every purchase intention can be traced back to an individual.
Brands will then be able to trigger the appropriate marketing actions to accompany these purchase intentions in a timely manner until they are transformed, and will have to capitalize on different levers depending on whether or not they have already identified the individual.
A recognized individual will be able to be activated and contacted on more points of contact (e-mail, SMS, social networks, search engines...) than an anonymous individual, who will be contacted via on-site personalization, display and/or retargeting.
By leveraging a hot pretext such as purchase intention to contact the individual, and by offering them recommendations of the products that are most likely to interest them, identified once again thanks to predictive models, the marketer takes all the necessary steps to generate sales during this delicate period.
In the face of ever-increasing upheavals, it is clear that retailers are increasing commercial pressure to encourage their customers, who are usually in-store buyers, to go online. The practice of individualized marketing, respectful of consumers because their desires and needs are recognized, is undoubtedly the key to converting its "store" buyers into "omnichannel" buyers.
It represents an opportunity for brands to secure their margins and to shift, as far as possible, part of their store turnover to e-commerce turnover and to take all the necessary measures to restore customer relations as soon as possible when the company returns to the retail market.
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