CONTRIBUTOR
Chief Technology Officer,
SugarCRM

A successful customer experience strategy should go hand-in-hand with enabling technologies such as customer relationship management (CRM) solutions to optimize and personalize engagement throughout the customer journey.

 

However, it’s been found that the accuracy of CRM data diminishes over time by approximately 30 percent a year. That means many organizations are trying to stay abreast of customer needs by looking in the rear-view mirror – hardly a way to demonstrate real-time relevancy in sales, marketing, and service.

Modern platforms infused with artificial intelligence and automated data acquisition are dramatically remodeling traditional CRM systems – eliminating the onus of manual data entry. Such that moving forward, it’s possible the best data in your CRM may be the data you never entered.

The Implications for AI and ML and Your CX Data Strategy

CRM software is the backbone for sales, marketing, and service professionals to personalize outreach and better track deals, engagement, and customer needs. However, we spoke to 1,600 sales and marketers around the world and 56 percent of companies surveyed feel their CRM system is missing data to improve their marketing campaigns and sales pipeline.

Today, it’s incumbent on users to properly enter data and to keep it fresh. Yet, the more data that you have to enter, the greater likelihood of error and the less time employees can spend selling, marketing, or providing customer support. While on the other hand, the more data you have in your CRM system, the better chance you can find opportunities, learn about customer issues, and tune marketing and business models to grow your top line.

The dichotomy represents one of the most fundamental challenges in CRM – the more data you enter, the less time you can spend making use of it, and the less likely it will all be accurate, since it ages at an alarmingly fast rate.

Innovations in AI and automated data acquisition are helping solve this age-old problem, reaching inside and outside the organization to automatically find loads of customer information and deliver it directly to the CRM – eliminating manual entry and extending the other half-life of your data by automatically keeping it updated.

The Analogy of Scale Conundrum

One of the most critical use cases for AI in the CX arena will be changing the ratio of time employees must manually enter data to the data that’s automatically aggregated.

There are many AI models that set precedent here. Take for example, weather forecasting, which relies solely on data from sensors, weather buoys and other Internet of Things (IoT) devices. These are both more reliable and predictable than any attempt by humans to compile data in this regard.

When it comes to CRM, however, we continue to leverage the error-prone, and often biased data provided by human operators. In fact, inputting data into business systems – the daily “dog paddling” of manual keyboard entry – is most often seen as a tax on employees’ time – conditions which are rarely conducive to producing high-quality data.

The reality is the scale of data required for an AI model to make significant contributions (insights, predictions, sentiment analysis) to inform business priorities is a task that far exceeds what is humanly possible today. Today, data strategies must go beyond human input and rely on as many data sources as possible: Firmographic, geographic, financials, professional data, and more are critical sources for building high-quality AI models for sales, marketing and service. Modern automated data acquisition methods are needed.

AI AutoML systems provide methods to determine what information and what models should be selected to generate the highest quality results and decisions. Meanwhile, modern AI techniques are excellent at determining what factors most influence high-quality models. For example, a business opportunity may be affected by the weather, or equity pricing. AI can use historic correlations to determine if this information is germane. Still, you always want to err on the side of data inclusion and let the AI sort it out.

Making Sense of “Big Data” to Fuel CX

Now that you’ve collected the data you need, how can you make sense of it? This can be a perennial issue for businesses, with the average company holding on average 162.9TB of data. AI is the key to understanding the data, creating a competitive advantage, and delivering an unparalleled level of predictability across a whole array of different business use cases.

Contrast this to life before AI and machine learning – where humans interpreted and decided what information was germane. Whether correct or not, based on capacity and bias, humans can only process a narrow slice of information when deriving insights or making decisions. And, let’s be honest, humans also forget things. Information and experiences that may be critical to forming a conclusion may be overlooked or simply forgotten in a critical moment.

The foregone conclusion is that AI is used primarily for prediction. But AI can help organizations understand the near and the now. When it comes to sales, marketing and service, most companies have copious amounts of data, but the human brain is not wired to make sense of it, to understand trends and find outliers in a given dataset.

The Future of CX is Data-Driven

Customer experience is and will continue to be at the forefront of business priorities, for it’s a decisive factor in boosting brand loyalty and increasing revenues. Still, customer retention can be surprisingly challenging when the experience just doesn’t match the expectations. Innovations in AI-driven CRM platforms are helping businesses automate anything, accelerate everything, and predict what’s next – exceeding customer expectations by bridging the data gaps today. 

 

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