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Evolve or Die: Personalisation and AI in Sales and Marketing

Evolve or Die: Personalisation and AI in Sales and Marketing

Irina VasilenkoAuthor: Irina Vasilenko, Product Manager, Retail and eCommerce, Ciklum. Irina has over eight years of experience in product management in different domains including Retail and Digital Commerce, MarTech, FinTech. Seven years of digital marketing experience. SAFe 4 PO/PM. Irina is a manager of such Ciklum products and solutions as Sales Booster, Loyalty, Retail Unified Commerce Kit.

An important attribute of product marketers and sales managers is the ability to adapt and respond quickly to a changing situation. Artificial intelligence (AI) is actively taking root in our lives, and the world of sales and marketing is changing at a rapid pace.

So how can we use it to our advantage? 

The customer has changed

The long-term use of social networks has changed the way millennials and Gen-Z think about and consume digital content. Concentration spans are shorter, with viewers constantly switching between different content and devices. Consumers don’t want to wait for a response, and if requests are not satisfied quickly, they will go elsewhere. 

A smaller screen size, viewing on the go, and limited attention spans mean the elevator pitch is becoming relevant for digital – a short description of a product, idea or business.

Our potential customers want to feel included in the conversation and to always be in touch. The client has changed and if we want to gain and keep their attention, we need to take into account new patterns of perception.

AI provides a huge opportunity for us to give our customers what they want.

The AI Experts

Leaders in the development of artificial intelligence systems are the technology giants – Google, Facebook, Amazon, Microsoft, Netflix, Apple, Nvidia.

Google’s dedicated AI R&D unit – Google Brain – has developed the largest open-source library for training neural networks – “Tensorflow”. Google spends tens of billions of dollars annually on AI development. Voice assistants and computer vision systems are already recognized as some of the best solutions on the market. 

Google’s state of the art recommendation models are already seeing success for Youtube and Google searches. Recommendation systems are at the beta testing stage for e-commerce, so that it too can benefit from AI. 

Facebook has a division specialising in AI – the Facebook AI Research Lab (FAIR). Since Facebook’s revenue generator is advertising, the unit’s main tasks are text classification, and image and video recognition.

Google and Amazon leverage AI to build state of the art recommendation models for digital commerce.

Amazon embeds AI in all of its products. Amazon AI Labs is engaged in computer vision, Neuro-Linguistic Programming (NLP), personalisation and forecasting. It was one of the first to launch a recommendation system for its own marketplace. According to a recent McKinsey report, more than 35% of Amazon sales come through a recommendation system. Besides marketplace, content personalisation also works on Amazon’s fashion resources – Zappos, 6 pm and others.

Tech giant Microsoft is actively evolving its own developments in computer vision and forecasting, based on historical data.

Thanks to developments in AI, Netflix are surely leaders in the personalisation of content. Subscribers are wooed not only by high-quality TV shows but also high-quality content recommendations.

The forerunner in content and advertising personalisation for media resources is Taboola, with Bloomberg, USA Today, NBC and others on their client roster. 

How personalisation works

The system collects data on user behaviour based on consumer touch-points and events e.g. what’s interesting to the user, and what have they previously consumed from a catalogue of data for goods, services or content.

Once data is collected, the magic of deep learning occurs. Artificial intelligence models build relationships between each product and each user. Based on large volumes of data, the models determine the patterns of user behaviour offline and online and build a huge number of forecasts such as which product may interest this particular customer in a given place at a specific time. It then builds a recommendation based on the forecast defined as the ‘most likely’.

Personalisation in sales and marketing

Advertising is becoming less effective and more expensive for businesses. Thanks to ad blockers, it’s getting harder and harder for us to acquire and maintain our customers’ attention. And the abundance of advertising banners has led to advertising blindness – when the user simply does not see a video or banner.

Personalised marketing messages are the future to successful advertising. They give the customer a sense of value and individuality and result in a significant increase in Click Through Rate (CTR), sales conversions and other growth indicators.

Email marketing is considered to be the cheapest advertising channel, but it is becoming more and more difficult to attract consumers’ interest. The open rate is lower than ever, and the effectiveness of email marketing as a conversion tool is dwindling.

But don’t ditch email newsletters altogether just yet, because they’re still a very useful marketing tool when they’re integrated with a highly effective recommendation solution. Each customer receives desirable and compelling individual offers – keeping them interested in your newsletter, your products and your services. 

This is also a great opportunity to remind the customers about forgotten products sitting in their shopping basket.

Sales managers are already feeling the benefit of AI through Customer Relationship Management systems, with the artificial intelligence built-in models automatically determining the likelihood of a conversion. They suggest what kind of service or product the client will buy with a higher probability, and what message will be the most relevant for the client. Resulting in much more focused targeting from sales managers.

AI can also be very useful in the work of a product marketer – finding relationships between events and subsequent customer behaviour. For example, we can see the correlation between the number of touches, communication type (mail, call, push notification) and the probability of purchase among different client segments.

Personalisation can also breathe new life into loyalty programs, which have become ineffective for many online and offline retailers. Loyalty programs don’t affect sales growth or income anymore, as they are primarily based on discounts or accumulation of points. But with the help of personalisation, loyalty programs can be reborn as a sales engine for the retail business.

AI modelling works by knowing the consumer better than they know themselves. The most conscious of us are well aware of how conditioned our behaviour is, and how many unconscious influences there are on our decision-making. But AI acts completely without bias. Moreover, it can predict your desire to buy before you even realise it yourself. 

It sounds intrusive, but it is impossible to deny the development and power of technologies,  and it makes sense to accept and embrace them to benefit your own business. 

It becomes even more relevant taking into account the current market situation that forces consumers to switch from offline to online dramatically. 

This is an opportunity for marketers and product managers to rethink their approach and shift their focus away from the promotion of random products to the user, in favour of offering the client what they actually want.  We need to move away from offering products to all users, towards opening the catalogue of hundreds of millions of products tailored to the desires and preferences of each individual customer.