Media reports on advances in artificial intelligence (AI) often cause negative reactions: the transformative potential of AI across industries is contrasted with sci-fi thriller-like scenarios of robots taking over the labour market, which causes (un)founded anxiety about losing one’s job and bleak employment prospects with outdated skill sets. “In practice, however, AI isn’t scary. Institutions that will be best positioned to succeed in the AI-fueled future are those who view advanced analytics, AI and machine learning as something that should complement client-facing teams, not replace them. They will see the opportunity to combine the power of both human intelligence and artificial intelligence,” says Craig Dunham, VP and GM of Financial Services at Seismic, a provider of sales enablement software. He gives 3 examples of AI-enhanced marketing in the banking industry:
- Automated content compliance. Machine learning algorithms can identify linguistic similarities in documents and classify them according to certain criteria. As a result, the compliance of information is checked instantaneously, regardless of when it was last updated. There is also an auto-tagging function based on a region and product offering, which helps better organize and customize content for marketing purposes.
- Data-driven recommendations and insights. An AI-powered platform can automatically process customer data collected during previous interactions with a bank and give accurate recommendations on what kind of marketing content will work best in a given context. A lot of factors are taken into account: an opportunity stage, used / offered products, a geographic region, a risk profile and financial objectives, to name a few. This helps “shorten sales cycles and enhance the entire client experience”, notes Dunham. The platform can also process data from all brand touchpoints and product lines to check the effectiveness of marketing content and make some changes if needed.
- Accelerated onboarding. AI helps streamline the process of onboarding not only new customers but also new employees. AI-powered tools bring new staff members to a productive level faster. “Knowing that they are working off a proven content blueprint that generates results, new relationship managers come out of the gate with both the tools and the confidence to succeed from day one,” explains Dunham. Thanks to the smooth transition of new entrants into the workplace, recruitment efforts can be scaled up.
Automated content production
Until recently, only a few media organizations specializing in quantitatively oriented domains such as sports and finance were using natural language generation (NLG) software, which processes raw data and turns them into coherent, readable texts. Many executives of AI marketing firms see high profit potential of NLG applications over the next 5 years. Not only will they cut costs on routine writing, but it will also change existing content (or create new content) to enhance marketing effectiveness. For example, there is the option of personalizing information for each customer segment. Content can be easily altered to better reach a target audience across communication channels by highlighting product features which correspond to specific needs of that audience, explains Laura Pressman, Marketing Manager at Automated Insights, a provider of content automation services.
Conversational user interface (UI)
Over the last 20 years, the interaction between companies (brands) and customers has mostly been conducted through a computer screen using a keyboard. However, the transition to human-machine interaction is already taking place. “The conversational UI is going to be an even bigger leap in software than we had with the shift to Web-based software. We are all re-thinking now how to build products,” says Dharmesh Shah, Co-founder and CTO at HubSpot, a provider of inbound marketing software. To make business-customer interaction more efficient, conversational UI will combine voice input and visual output. “We will have voice input because it’s much more efficient [than typing] and visual output because it’s more efficient than listening – we can see and read and scan much faster that we can listen,” Shah explains. He also predicts that interactions through computer screens will continue, but interactions using keyboards will become less common.
Many sales teams face the challenge of timely follow-up on each marketing-generated lead. “Qualifying and nurturing a lead is time-consuming, with a lot of repetitive activities. On average, sales reps spend 80 percent of their time qualifying leads and only 20 percent closing,” observes Alex Terry, CEO of Conversica, a provider of lead engagement software. To qualify leads, sales teams need to conduct thorough research and spend a lot of time on phone and e-mail follow-ups to turn leads into sales. This workload can be passed to AI software, thus freeing up employee time to close more leads.
According to the marketing Rule of 7, a potential customer should hear or see a marketing message at least 7 times before they decide to buy a product. Terry estimates that on average only 12% of sales reps make more than 3 contacts with a prospect. In his opinion, AI-driven lead conversion can help reach the recommended number of engagements per customer to increase brand awareness and sales prospects.
Research shows that if customers are engaged in real conversations at an early stage of the lead-to-sales conversion process, they are more likely to form a stronger connection to a brand and share more commercially valuable information about themselves, which in turn leads to higher conversion rates. The results can be improved even further if AI lead conversion software is incorporated into CRM and marketing automation software. This solution increases the number of qualified leads as well as easily identifies people who show no interest in purchasing, Terry notes.