In today’s world, where AI is helping optimize all engineering processes, we should consider the possibility of employing these techniques for marketing and sales activities as well. It is not that they are not employed today. But, there still is a lot of scope to scale up and generate higher profits by using AI on a much larger scale. Today, primarily AI is used by online sales companies like Flipkart, Amazon, and Netflix. These companies have reported a sky-high return on investment(ROI) for using AI to boost their products and sales performances.
With the large amount of data in our hands, we can develop sophisticated models that can predict user behavior and also suggest the best course of action. The performance of these highly optimized models is far better than that of humans. Even then, they have not been exploited to the fullest by all sectors.
One essential application of AI in sales is to help improve the productivity of salespeople. These algorithms are much faster in identifying and monetizing customers by studying the behavior of customers. They can help a company cut down the workforce that is being employed for the same task and not producing results comparable to that produced by AI algorithms. You need a human to validate whether the suggestions made by the algorithm are correct, but this helps improve the company’s productivity by leaps and bounds.
Why then have most companies not shifted to AI-centric models? Like all business models, such a model has its own set of challenges that must be overcome to be successful.
Building a Team with Proper Skill Set
It is imperative to build a team comprising of people who have a sound knowledge of computer science and statistics and have a good idea of economics. The team also needs boundary spanners- people who understand both technology and sales.
Data Integration from Diverse Sources
Data collection and integration is one of the biggest challenges in building machine learning models. Typically, more massive and diverse datasets lead to better results as more generalized models are built. Different sources help eliminate the issues related to bias and overfitting, thus creating a generalized solution to the problem.
Intuitively, a complex AI implementation should involve agile, phased, and iterative design and testing, with ongoing feedback from the sales team members. It is one of the most common errors made by new companies.
Further, it is essential that the output of the algorithms can provide the sales analysts with some relevant metrics which they can use to make their decisions. It is crucial that the sales team can relate the importance of the output of the algorithm to the real-life market problems.
Once a company can work out a sound plan to integrate AI into its model, the next step is deciding where it can be applied. The company must focus its resources on established applications of AI instead of working on experimental areas, as their capital is limited.
Using AI in Sales and Marketing
Some typical applications in sales and marketing include:
- AI-powered chatbots
- AI-enhanced image search features which help users find the product they find interesting. Google’s image search engine has implemented this feature.
- Personalized solutions to the client, depending on the product you are selling. One excellent example of this is UnderArmour. UnderArmour leveraged IBM Watson’s AI to create a “personal health consultant” that provides users with timely, evidence-based coaching around sleep, fitness, activity, and nutrition.
- Targeted advertising and pricing of products: Advertising products dependent on user behavior on the subject website. At the same time, a product can be priced dynamically depending on its demand and supply to maximize the profits.
Another major issue in client-based products is ensuring that legal hassles are minimized. In cases where the product would be used in a B2C industry, it is necessary to have strong consumer data policies in place to avoid lawsuits. Data based lawsuits have become quite common in the last few years, the most famous being the Cambridge Analytica scandal. To prevent such lawsuits, your product must comply with the General Data Protection Regulation (GDPR) and any other regulations depending on the country you are targeting.
The consumers should be able to interact with connected devices — from web browsers to mobile phones to voice assistants — knowing that their data is being used in transparent ways, in a manner consistent with their preferences and expectations, to which they have explicitly consented. At the same time, data security is a significant concern that must be addressed by using such products.
Once you have a sound system in place comprising of proper policies, data assets, and pipelines, the stage is set for using AI to make fast and efficient business decisions using AI. In case, as a company, you rely on third parties for the service, you must be very careful in evaluating the knowledge that the framework of these organizations provide you and how useful they would be to your company.
If appropriately used in sales and marketing, AI can help improve the performance of a company by leaps and bounds. But, if you fail at some point in building a sound AI ecosystem for your product, it may have catastrophic effects on your company. Such screw-ups may even damage your company’s reputation, as happened in the case of Facebook when there were claims that the user’s data was used to spread misinformation and trick the people of America.
- Harvard Business Review