2022 will be a memorable year in history, not only because of the ongoing pandemic and its aftermath, but also due to the significant advancements in Artificial Intelligence (AI). While AI still has its limitations, the chatGPT technology has demonstrated the potential of large language models and how they can be applied in various aspects of our lives. In 2023, the increasing adoption of AI will bring both opportunities and challenges. Those who are ready to embrace the technology will have a competitive advantage, while those who are slower to adopt may face difficulties.
AI is becoming more mainstream and will have a widespread impact on all aspects of life, both human and non-human. In the past, automation has been driven by the need for speed and processing power, but chatGPT has made large language models more accessible, enabling a wide range of AI applications. This development raises the question of what will happen to companies like Google, which is reportedly treating the situation as a “Code Red” emergency. The impact of chatGPT and other AI technologies will likely be felt most strongly in industries that are yet to fully embrace digital tools and transformation. In fact, the ability to adopt higher paradigms like AI may become a matter of survival for some organizations.
Manufacturers and Industrial OEMs might think that with their scale and reach, they are in a safe position. The problem is the speed and intensity of disruptions that new technologies are bringing. Industrial OEMs do appreciate the need for digital transformation, but it is still not seen as an urgent priority. Most of the industrial OEMs are focussed on their top customers. And many times, they don’t know how to deal with long tails.
- Research by McKinsey has shown that each percentage point by which services grow over product sales correlates to a 50% increase in enterprise value.
We can state a corollary here. When an organization fails to fully monetize its wallet share in the aftermarket, it is allowing others to enter its customer base. This also has implications for future equipment sales.
Aftermarket is essential for the financial health of an enterprise. The question, however, comes how we can leverage digital tools and AI for doing aftermarket sales and services.
Let me start with the boring topic of data quality. Most OEMs think that they cannot do anything because their data is a mess, and there is no way to fix it. That is no longer the case. With many AI-driven data quality digital tools, that is easily achievable. With Entytle’s Data Quality Engine, we have seen multiple success stories. We have seen successful data quality projects where the data is cleansed, enriched, deduplicated, and unified. We have seen a 30-50% reduction in data size because of duplication. 100 million + records across assets, parts, and transactions have been unified successfully to build a 360 view of the Installed Base.
Once the data quality is in place, other things fall in place. AI can help with a data-driven approach to aftermarket engagements. It can help with diagnostics as well as data points for future engagements.
Here are a couple of things where AI can really help out
AI can help with segmentation and clustering customers in multiple ways. This helps in defining strategies around different segments. One of the most effective ways is to segment them around the notion of loyalty. The segmentation can help in identifying drifting customers as well as those who are showing promise for growth.
Leads and Opportunities
Equipment work on duty cycles. For a given operating condition, the same equipment behaves in a similar way. They have similar needs for consumables, services, and parts replacement. AI algorithms can look into the consumption data patterns into similar cohorts of customers and predict the opportunities. When someone is not buying from you might as well mean that they have gone to a competitor. Time to recapture the wallet share!!
Propensity to buy
Let’s say the AI system delivers two opportunities for $20,000 each. Which one to go after if resources are available for only 1? Propensity to buy can help in that as it will tell the likely probability of the prospect buying it. This helps in prioritizing the efforts.
The sales team loves to upsell and cross-sell. But which items to bundle together. AI can help with recommendations. It can look into the transaction histories and find out what people buy together.
Whom to call
Everything is in place. The predictive opportunity is there with the list of recommended items. The propensity to buy shows a high probability, but how to decide whom to call? For top customers, we carry their phone numbers in our pockets but that is not true with a long tail. AI can help in surfacing the contacts, and with the appropriate weightage model, it can create a calling sequence. Based on how the contact was mined, it can even recommend if the contact is appropriate for a service or a part sale.
Data-driven approach coupled with AI can unlock the value of the installed base in the aftermarket. It helps in building and enriching the connect with the customers which further helps in future equipment sales.