So, how exactly business uses of or plans for AI?
It is a well-known fact that eCommerce has always remained one of the most customer-facing industries. But it is also true that customers’ expectations for smarter interactions has been rising. In response to these rising expectations, businesses have been trying to find ways to infuse AI into everything ranging from customer experiences to internal sales operations.
So, how exactly business uses of or plans for AI? For one thing, AI promises to re-architect business models — enabling companies to get closer to their customers, design hyper-personalized experiences, all of which results in delivering relevant customer engagement in real time.Apart from this, one of the important facts that must be mentioned is that AI and machine learning clearly has demonstrated the potential to reduce the most time-consuming, manual tasks that keep sales teams away from spending more time with customers.
AI technology has contributed to a situation when customer expectations have skyrocketed. Sales teams adopting AI are seeing an increase in leads and appointments of more than 50%, cost reductions of 40%–60%, and call time reductions of 60%–70% according to the Harvard Business Review article Why Salespeople Need to Develop Machine Intelligence. In the sales department, intelligent selling has already been driving productivity improvements and increasing the speed at which sales teams operate for quite some time now.
AI-powered developments such as, for instance, individualized recommendations and automatic order fulfillments are constantly raising the bar against which customers tend to judge companies.
Currently, 55% of consumers and 75% of business buyers expect personalized offers. By the end of 2020, 51% of consumers and 75% of business buyers are going to expect companies to anticipate their needs and make relevant suggestions and as many as 30% of all B2B companies will employ AI to augment at least one of their primary sales processes according to Gartner.
What about customers’ point of view? It is predicted that half of consumers will abandon those brands that don’t anticipate their needs. However, only one-third of consumers, on the other hand, is able to name an example of AI they keep using on a regular, day by day basis. Data proliferation, paired with increasingly refined modeling capabilities, is making AI a mainstay of everyday life and, as a result of this, expectations are currently being fundamentally altered.
Sales, service, and marketing leaders mostly view AI as a tool to drive better customer engagement and increased revenue as well. This can be explained with the fact that High-performing sales teams are 4.1X more likely to use AI and machine learning applications than their peers according to the State of Sales. It seems that it is AI that is helping sales organizations make that critical shift from simply facilitating transactions to building lasting relationships with customers
If we were to name but a few out of the most important areas of its use, we would put intelligent forecasting, opportunity insights, and lead prioritization on the list as the top three AI and machine learning use cases in sales.
It generally seems that the role of sales is no longer that of an order-taker. To succeed in the Age of the Customer, sales teams are focusing on driving trusted advisor relationships with customers through personalized and proactive engagement. Top sales teams — those driving significant revenue growth under this new dynamics — are more likely to leverage AI. In the meanwhile, since AI and machine learning technologies keep excelling at pattern recognition, which enables sales teams to find the highest potential new prospects by matching data profiles with their most valuable customers.
Personalization through AI also has a financial upside to offer: online shoppers who act on AI-powered product recommendations yield 14% higher average order values. Except for this fact, to win sales and increase loyalty retail and consumer goods marketers are striving to engage consumers in the real-time, conversational manner they expect. Because they need to understand who customers are and what actions to take based on their unique needs, retail and consumer goods marketers are increasingly using second-party data — that which is shared between consenting parties such as, for example, brands and publishers — to extend audiences and refine growth targeting.
It is becoming more and more obvious that, considering all of the above-said, without AI brands may be missing this key opportunity to not only attract consumers, but sustain shopper satisfaction, loyalty, and advocacy.
Lead scoring and nurturing based on AI and machine learning algorithms help guide sales and marketing teams to turn Marketing Qualified Leads into Sales Qualified Leads — and, apart from this, it also contributes to strengthening sales pipelines in the process. Since Personalizing sales and marketing content that moves prospects from Marketing Qualified Leads to Sales Qualified Leads is continually improving due to AI and machine learning, lead nurturing strategies that move prospects through the pipeline, which is one of the most important areas of collaboration between sales and marketing, have been on the rise as well.
Although marketing departments can be really well-versed in using personalization to build customer engagement, technological developments have raised the bar on what constitutes a truly personalized marketing experience.
In their quest to deepen customer relationships, many marketers have moved from simple segmentation to dynamic content strategies, powered by machine learning, to tailor communications and offers to individuals. And this has been done predominantly because dynamic content simply presents marketers with an unprecedented opportunity to engage individuals on their unique customer journeys. What’s more, it is believed that the level of this engagement is something previously unheard of.
AI and machine learning are enriching the collaboration with insights from the third-party data, prospect’s activity at events and on the website, and from previous conversations with salespeople. In order to help improve each lead’s score, lead scoring and nurturing relies heavily on natural language generation (NLG) and natural-language processing (NLP).
Both artificial Intelligence (AI) and machine learning have definitely demonstrated the potential to reduce the most time-consuming, manual tasks that typically keep sales teams away from spending more time with their customers. Among the techniques freeing sales teams from manually intensive tasks one could mention, first of all, automating account-based marketing support with predictive analytics and supporting account-centered research, forecasting, reporting, and recommending which customers to upsell first.
AI can certainly play a role for companies looking to deliver on elevated customer expectations. However, merely to recognize its AI’s potential isn’t enough. If what is needed is to derive real value, companies must first carefully examine how the technology will fit into their specific business processes.
Today’s systems of engagement, along with advanced capabilities like artificial intelligence, provide the necessary data and the ability to make it actionable.
Modern direct-to-consumer e-commerce companies build, market, sell, and ship their products themselves, without middlemen via Instagram. How could they employ the power of AI, then? Most easy way seems to be implementing AI CRM directly into sales process.
AI CRM can define a salesperson’s schedule based on the value of the potential sale combined with the strength of the sales lead, based on its lead score. AI and machine learning optimize a salesperson’s time so they can go from one customer meeting to the next, dedicating their time to the most valuable prospects.
Our AI-powered chatbots keep leads engaged by instantly answering their most typical questions, but what is more important is the CBM as such — Cash Bot Machine by BRN.AI — a set of intelligent tools that include all you need for successful eCommerce, including Social affiliate system, Smart sales recovery, Sales Analytics, Smart CRM and integration with a Shopify-based shop. Last but not least, CBM’s no-code bot builder makes it considerably easier easy for anyone to create their own Instagram direct-to-consumer company.