In this blog, you will get an insight into Artificial Intelligence within CRM and the misconceptions about AI and CRM.
AI and CRM
AI and CRM Introduction
Artificial intelligence within CRM
Artificial intelligence within CRM seems to be the new buzz, just like the Internet was the new digital wizard a decade or so ago. And, at a time when businesses seem to be trying to gain customer attention amidst fraying loyalty trends, using AI in their Customer Relationship Management plans may seem to be the perfect answer to get that winning edge.
However, there is more to it behind all the hype. AI is not something that has popped up suddenly, the concept has been around for decades. It is just that with the current technology and digital boom, the usage and application of AI have become more practical. However, it is important to understand some practical challenges and realities behind the capabilities of Artificial intelligence within CRM, especially how they can be applicable to the realm of CRM for a business.
Misconceptions about AI and CRM are listed below
AI is near the human brain
AI leverages the ability of algorithms to ‘learn’ from huge volumes of data and find patterns or trends. With the social media and digital data accessible in both structured and unstructured forms, suddenly there are huge volumes of data available to ‘tune’ the AI algorithms to understand customer behaviour, preferences and perhaps push products that are AI-powered to customers. These can pave way for higher conversions and increased revenues. However, Artificial intelligence within CRM is far from being equal in intelligence to the human brain this is one of the misconceptions about AI and CRM, it is just a fast aid to sift through huge swathes if data at extremely high speeds to achieve fast results.
All data needs to be parsed by AI, so it will mean extensive storage need
Misconceptions about AI and CRM – Storage of data is a costly affair. There cannot be any intelligence without data, that is true. However, data which is not used well is a wasteful investment. Rather than storing all types of data, businesses should carefully consider the utility aspects of their customer data sets and which ones should be applied to AI-based systems like predictions and analytics. In this form, the AI solution is still wholly dependent on the human perceiving capabilities.
Too much data can be overwhelming, so focusing on smaller sets makes the whole thing more manageable and practical. Reverse engineering also helps in selecting the right type of data to start with for using AI for a particular business goal.
AI is related to CRM, hence it is beneficial to specific functions
The new business model is set to pivot on the customer-centric approach. This automatically translates into the applicability of CRM data across all functions of an enterprise.
Of course, with time, the synergy of AI and CRM will become more seamless and integral. The traditional approach to use CRM insights into silos like sales, marketing, customer service, etc. is now obsolete. Instead, an integral approach that links all departments across shared data flowing seamlessly across all workflows is the key to a true usage of insights from AI. For this, companies will need to have data synergy strategy as a stepping stone to adopting AI. These data silos can actually be hurdles to AI adoption, so tackling them early will be a wise and informed choice.
Collation of customer data is the first step to AI adoption
With this in mind, companies may start to invest heavily in getting the ‘right’ type of data before starting to play with AI. This approach is very dangerous and can play as a catalyst for catastrophe in case the AI pilots fail to bring the desired results. Rather, it may make more sense to start with getting actions mapped to already available data via AI algorithms and see how the returns play out on business goals. In this way, you can ‘learn’ the aspects and abilities (as well as limitations) of AI,just as AI will ‘learn’ about your data!
Organizing data is a precursor to successful AI
True, customer data is incidentally flowing into the systems in all possible forms, much of it is unstructured or in deep heaps. Even databases may have random and disorganised datasets. This, however, does not warrant a huge exercise to ‘chastise’ all data before you seek out AI. Rather, when decisions for data selection come from front to back, e.g. customer-facing problems or improvement of customer interactions, for instance, the support systems and processes automatically come under scrutiny. These may or may not warrant data cleansing efforts, as the processes may themselves be updated in a bid to add intelligence.
AI is expensive
This totally depends on the extent of the solution selected. A corporate can start with a simple and limited investment, test waters and then delve deep into the right direction. Such calculated risk can expose newer avenues and areas and the learnings themselves may be the ‘soft’ returns as they can help make a better choice for bigger AI investments, even if it means that there is some failure encountered in the initial prototypes. In fact, with support like cloud-based apps, the AI costs may be reduced and scalable both upwards and downwards for maximum cost optimization.
Here come the another misconceptions about AI and CRM. At the outset, AI is not a technical superhero that can open deep insights for informed CRM based decisions. CRM tools and apps that feed data and data patterns into AI-powered analysis and dashboards can definitely boost a business, help leaders make predictive and measurable targets. However, the right AI solutions are needed to convert customer data into deep insights and actions. One method could be to start in small phases, start with quick wins, such as next-best recommendations, and then move on to bigger insights that sift through more customer data and deeper history.