Every company has got some automation — if not artificial intelligence (AI) — initiatives going on. Assess where you are in the progression and where you might want to go. Below is a list of critical use-cases on a road map to AI adoption, along with the complexity and advantages gained. Sales and marketing processes amenable to AI can be divided into these areas ordered from least to most complex:
1. Customer Support
Two exciting use cases for AI in supporting customers are chatbots and sentiment analysis.
• Chatbot: Remember calling a support phone line for a credit card or service? Remember when there were first just numbered options spoken in a mechanical voice that needed to be navigated? Have you run into one yet that lets you ask any question and reacts with an appropriate answer? The chatbot is an extension of this idea but reacts to typewritten queries using natural language processing (NLP). The hiccup is in how it handles strange syntax or incorrect spelling. Dedicated chatbots narrow the possibilities by being domain-specific where the possible vocabulary is somewhat bounded. Questions that the chatbot cannot handle need to be fielded by a human, but substantial cost savings result nevertheless. Chatbots are technically easy to adopt into a customer support system. For example, some banks now have chatbot support.
• Sentiment analysis: Unstructured text on social media and customer service channels can be analyzed in real time to determine things like collective brand sentiment and alternative support responses and can even be used for PR planning. This technology is also based on NLP. Integrating this into a chatbot allows for even greater relevance and accuracy in automated customer support. Gaining wider adoption, sentiment analysis is being taken further with facial analysis to determine unstated emotional reactions.
Segmentation-based market messaging is a thing of the past. AI allows much more accurate one-to-one messaging and intelligent pricing. These are technically heavier lifts requiring dedicated infrastructure and sophistication.
• One-to-one content marketing: AI in combination with a good customer 360 database actually allows for individualized messaging with content that is most likely to coax a potential customer further down the funnel or to become repeat buyers. With the addition of customer purchase and support data to customer 360, AI or machine learning (ML) can gauge customer intent (even when not logged in) in real time and serve the most relevant content, such as comparative information, how-to, best offer or loyalty perks. This can be implemented at multiple touchpoints in the customer life cycle, enabling much more efficient throughput in the funnel. However, companies have been attempting customer 360 for a while. This process has to be hastened and connected to ML. This is an investment- and effort-heavy step. Dedicated change management along with appropriate support from the C-level is required.
• Dynamic pricing: Whether based on location, time, persona or value, dynamic pricing can lead to long-term revenue efficiencies that fixed pricing cannot. By considering demand, purchase and demographics data, AI can provide a uniquely validated and individualized price point. Inventory and margin considerations can also help create much more holistic pricing. This a huge change to normal business processes and operations. It will also require multi-department cooperation and buy-in from all.
Unprecedented opportunities in automating sales become available via AI. Add in market condition analysis and prediction to put market domination in reach. This is the final stage of AI adoption and will require both effort and sanction from all, including the board.
• Sales automation: Automated sales skills are on the cusp of implementation. Imagine a dedicated sales agent who would instantly know every customer’s history with your company and be able to gauge their current intent. Imagine this dedicated agent being able to negotiate and sweeten the terms of the sale with alternative perks. Imagine engaging a potential customer like this any time and from anywhere. Automated and individualized pricing or perks, along with upsells and cross-sells, can increase sales pipeline efficiencies enormously. With an appropriate mix of customer 360, sentiment analysis, social media and/or internet tracking and a chatbot, this is actually possible now. While debates and regulations about data privacy and ownership continue, there is enough data with identity removed that can be utilized with minor variations in the way that it is delivered to create this full picture of most customers. Salesbots have already been tried. Now, they just need to be honed. Training salesbots will not be unlike training a sales force. It will, however, probably be much more cost-efficient. All of the infrastructure and sanctions built in the previous steps on this roadmap and more will be needed.
• Market prediction: Reports created by external market analysts have been bought and used by companies to arrange their strategies. The reports were generated externally and consequently made somewhat generic. I believe the new world will witness the purchase of data rather than reports. Companies will research and analyze directly to predict market trends and opportunities unique to their landscape. This will unveil any market domination possibilities much more quickly and clearly. The analysis will require a budget and low-level sanction, but strategy around it will need board-level approval.
The first steps in the above road map are local to departments, but the remaining start to touch the whole company. So, who in the company should drive this road map? While the technology needs to be specified by the CDO and perhaps built by the CIO, this is all customer-related and indicates CMO or CCO ownership. However, all scenarios are revenue-bearing and should really report to the CRO or the CEO. I believe we’ll see revenue generation change dramatically by AI in the next three years.