How can AI be used to boost sales?
Sales and Marketing Software

How can AI be used to boost sales?

How did one Harley-Davidson dealership in New York City go from selling one or two bikes a week to selling 15 in a weekend? Owner Asaf Jacobi took a risk on Adgorithms’ ‘Albert’, an artificial intelligence (AI) driven marketing platform that works across digital channels. The results saw the dealership increasing leads by 2930% by the third month and driving Jacobi to set up a new call centre to handle all the new business.

Albert can learn as he does and is able to “identify the audiences most likely to convert, eliminate low-value audiences, apply insights gained from one channel to other channels”, according to Harley-Davidson NYC. The AI works with campaign creative and KPIs provided by the brand to autonomously execute holistic digital ad and marketing campaigns.

 

Who is using AI to boost sales?

Harley-Davidson is not alone in its use of AI in sales and marketing. Around the world, companies in all sectors are turning to AI to help them beat the sales clutter and generate more leads and ultimately more sales. In Mexico, for example, Nearshore Delivery Solutions (NDS) is using IBM Watson and cognitive computing to analyse potential clients’ personalities, generate a risk profile and match them to the right sales agent for them. The data generated is based on the potential client’s social media activity and creates a personality profile based on the Myers-Briggs personality test. The AI analysis is being used by financial services provider to target potential investors.

Realistically, artificial intelligence is not actually all that intelligent - at least, not yet. Check out: Artificial lack of intelligence: how smart is AI really?

UK supermarket Morrisons is deploying AI in its ordering system to optimise efficiency and boost sales. It has partnered with Blue Yonder to use its technology to improve product availability. The ordering system is simpler, easy to use and is reducing shelf gaps by up to 30%, according to Morrisons. The system was launched in Morrisons during 2016 and now covers all 491 stores, automating over 13 million ordering decisions per day.

The Blue Yonder Replenishment Optimisation technology automatically analyses sales data and other data sources from Morrisons and combines this with external data such as weather forecasts and public holidays. Through the automated analysis of data, the system can predict the level of demand down to the individual product and store location.  Blue Yonder’s technology then fully automates ordering per store and per product.

Blue Yonder’s Replenishment Optimisation uses cloud technology, making it capital-light and highly scalable. Using machine learning technology, the system learns as it goes and can use a vast and complex amount of data to make highly accurate ordering decisions.

The benefits of the system are multiple: employees no longer need to spend time manually ordering goods, which frees up their time for other tasks, such as attending to customers and; with improved in-store availability, customer satisfaction improves.

 

How can data be leveraged for sales and marketing?

It’s little wonder that AI is being integrated across sectors in this way. Sales and marketing are driven by data and if there is one thing that AI is good at it is analysing and leveraging data.

“Smart marketing is driven by deep data, so naturally as the science becomes more sophisticated, artificial intelligence will shake up the industry,” says Mark Shore, co-founder and president of Strike Social. “Failing to invest in AI now would be a mistake for the C-Suite; there are simply too many areas across marketing that AI can streamline.

Strike Social’s machine-learning algorithms yield more optimised social ad campaigns for big brands and agencies. Typically, millions of dollars in ad spend is managed by junior-level employees, who lack the expertise to fully optimise campaigns.

“More alarming is how archaic their operations are, with tweaks to audience targeting and budget allocation communicated through email or managed with sticky notes. AI is taking over that process. Computers can now suggest smarter media plans and then shift ad dollars to areas where brands get the best ROI. This means more accurate targeting, at a better price, to people who actually want to see your ads,” Shore says.

AI also eliminates human error. Shore points out that too many media buyers have made at least one six or seven-figure mistake in their careers. AI solves that while freeing up media-buying teams to focus more on strategy.

Florian Lüft, VP of sales at Apptus, explains: “AI is picking up much of the heavy lifting in marketing; the sort of work that requires analysis and decision-making based on huge volumes of source data that humans couldn't possibly hope to work through. What's more, it does it in near real time. Executives need to be aware that there are many flavours of AI. From simple rule-based decision making to sophisticated machine learning, you can't just sprinkle AI over your operation and watch it grow.”

Anil Kamath, Adobe Fellow and VP of technology at Adobe Experience Cloud, says that there are several primary ways that AI is helping marketers become smarter and adds: “Creating personalised content at scale is critical for brands. In the future, marketers will increasingly unleash content velocity with AI, meaning they can use and deliver unprecedented amounts of content quickly.”

 

How can organisations maximise internal investment for AI deployment?

Continuum Analytics’ senior data scientist, Christine Doig, says AI in marketing is evolving from buying blackbox solutions to building internal marketing data science and AI teams. “Companies need to start investing in AI talent, so they can provide more customisable insights into their business and data,” she says. “Blackbox AI solutions can be a good starting point, but those products might not account for all the complexity of your business, and data, or be able to upgrade fast enough to the latest AI innovations, which are mostly happening in open source ecosystem.”

She adds that data scientists and AI researchers can really understand and validate the outcomes of the models to ensure that those results are validated and c-suite executives have all the context to understand how to make decisions based on the outcomes of AI.

According to the Harvard Business Review, companies that use AI in sales see significant value, including more than 50% increase in leads and appointments, 40% to 60% of cost reductions, and 60% to 70% reduction in call time. “For a group that cares about the bottom line, AI can help close deals faster,” says Cedric De Vleeschauwer, Director of Demand Generation and Partnerships EMEA, at Showpad, a provider of sales enablement technology.

De Vleeschauwer explains that the use of AI in marketing and sales is needed to personalise the buying experience to each customer, such as the latest sales enablement platforms.

To be most effective, De Vleeschauwer says, marketers should create personalised content for each customer at every stage of the funnel.

“For years, technology has helped identify what content works when, but humans still had to generate that content. Through the advent of AI technologies, such as IBM Watson, marketing can take the insights they’ve been gathering for years and use them in an entirely new way. Machines can synthesise that data and generate things like personas, personalised content, and recommendations, freeing up marketers’ time to focus on other priorities,” he says.

This is true for sales enablement platforms too. “Think about how any initial sales call could change if you had the technology to identify your ideal customers and find prospects that match your personas,” says De Vleeschauwer. “Everything could be personalised from the first outreach, which can then be scaled and used throughout campaigns. AI can identify customer pain points and recommend solutions, taking a lot of the guesswork out of prospect interactions.”

 

What are the pitfalls of AI for sales and marketing?

It is easy to get caught up in the hype, though, and end up with something that does not deliver what it promised. Pini Yakuel, CEO of Optimove, a science-first marketing automation company that harnesses AI to deliver tangible business and marketing results for brands including 1-800-Flowers, Freshly, Happy Socks, and Zynga, cautions of the possibility of purchasing a solution that is mis-billed as AI.

“For example, some solutions might be projecting a simple A/B test as ‘AI’ when it's not. The best way to use AI for marketing is to choose a technology that is truly fuelled by AI: these include technologies which employ machine learning algorithms to create dynamic, self-optimising segmentation, so you can segment your customers to a far more granular level than you could before, and then deliver emotionally intelligent communications to them that will impact business results,” Yakuel says.

Dr Janet Bastiman, chief science officer and artificial intelligence team lead at StoryStream, adds that one of the biggest pitfalls in AI is algorithmic bias and lack of adaptability. She explains that because AI systems often rely on being pre-trained with large amounts of data, they struggle to anticipate trends. Not only this, but they are also biased towards the data they were given. “If care is not taken in the training data then the AI may look good in testing, but give the wrong results when exposed to real data,” she says.

For example, Bastiman cites research by Carnegie Mellon University, which found that significantly fewer women than men were shown targeted online ads offering help obtaining jobs paying more than $200,000. “In marketing, particularly where products and consumer interactions change quickly, non-responsive AI can quickly decrease in performance,” she says.

Doig advises c-suite executives looking to invest in AI for sales and marketing not to buy AI solutions without a data science or AI research team that can help them understand their data needs and how to get those insights by using AI, and correctly interpret those results to make good business decisions.

 

What is the best advice for companies who want to use AI for sales and marketing?

For Kamath, what is most critical when implementing machine learning is domain knowledge of a brand’s own business and industry. He says that when evaluating AI technologies, c-suite executives must ensure vendors have the domain knowledge needed to use data in the right way to solve the right problems.

“Ask vendors if they have access to differentiated data and if they have an infrastructure in place to get the data in a way that’s usable. A strong AI or machine learning system alone will not benefit your business; differentiated data is critical,” he adds.

Using AI opens up the possibility of increased personalisation, but this must be approached strategically. As Ashish Koul, President at Acqueon, points out: “targeting customers with personalised deals is a reality, but there’s a fine line between coming across as useful and creeping customers out.” He says the c-suite must play close attention to how the company is contacting consumers – small details like the time of day customers are contacted, the kind of information that is shared, or even the communications channel that is used, can make a huge difference in how the message is received.

“Brands need to not only know what kind of offer to make, but to personalise how they contact that customer. For instance, a text offering a discount on your next coffee, which is sent at the time you normally buy your coffee, might seem creepy to some and helpful to others. It’s about understanding not only what a customer might want to buy, but also how best to communicate to them,” Koul says.

Qlik VP of global product marketing, James Fisher, believes that deploying AI is about making people smarter using machine intelligence without restricting them to predefined questions or analytical frameworks dictated by the machine. He explains that sometimes machine intelligence can point out statistically derived insights that prompt users to look at things differently, and ultimately remove data bias and broaden knowledge. At other times, Fisher says, users will anticipate and explore ideas using intuition, knowledge, and context that the machine doesn’t have. 

“As a result, simply relying on the machine, while helpful, may not be the optimum path to real business value. Best-in-class solutions will enable both to work together, to create a relationship where the whole is greater than the sum of the parts,” he says.

Michelle Huff, CMO of Act-On Software, a provider of adaptive marketing automation, believes that adopting AI is an opportunity to abandon the linear, one-size-fits most persona-based approach to marketing and adopt a more adaptive, individualised approach to customer engagement where machine learning aids in delivering personalised, one-to-one experiences at scale. “Doing so can positively impact customer lifetime value; by building better, longer lasting relationships sales can see both an increase in win rates and retention rates,” she says.

“Embrace AI and use it to your advantage; let it learn over time and be a recommendation engine for your sales and marketing teams – prescribing for them who to target, when to engage, how to score, and when to follow-up.”

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Bianca Wright

Bianca Wright is a UK-based freelance business and technology writer, who has written for publications in the UK, the US, Australia and South Africa. She holds an MPhil in science and technology journalism and a DPhil in Media Studies.

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