Predictions: what we got right in 2016 and what we expect in 2017

Every year in January we see a deluge of CRM trends and market predictions and we like to take a critical look at our expectations for the previous year to see where we got right and where we missed. At the beginning of 2016 we anticipated a few key markets would have significant impact on CRM - specifically intelligent business processes, Internet of Things, and Big Data. These were all areas of high growth and innovation in 2016 but we anticipate even more significant advances in 2017 as the technology becomes more mainstream.

While many of the advancements in CRM were incremental, 2016 saw the hype surrounding Artificial Intelligence (AI) surprisingly reach critical mass. Many CRM leaders (including bpm’online) announcing AI initiatives, and even Amazon got in on the action with Alexa products. 2017 will likely deliver even more news about AI, as industry analysts like BAC expect the Artificial Intelligence solutions market reach $153 billion by 2020.

We anticipate 2017 to be the year when many of IoT and AI finally go mainstream with the massive increase in connected systems requiring machine intelligence to help consumers and businesses manage these new technologies. So here is a run-down of our key predictions for the last year and what we expect to take off in 2017.

Intelligent business processes

In 2016 we saw companies investing more in CRM solutions powered by intelligent business processes. This enabled organizations to considerably simplify the day-to-day operations by automatically determining the best communication channels to engage customers as well to make data-backed decisions by incorporating previous knowledge into business processes. In fact, recent statistics by Harvard Business Review shows that 96% of respondents agreed or strongly agreed that machine learning is automating process-change management inside their organization.

In 2017 with businesses preparing for investments in AI, we expect to see companies expand their use of BPM technology. This year more organizations will be fostering data-driven process modeling to build seamless business processes, which will enable them to build better operational efficiencies and streamlined communications with customers across various channels.

By taking a data-driven process modeling approach, more organizations can optimize business results and improve forecasting. Additionally, this year we’ll also see the emergence of such AI-powered BPM-related tech as smart process analytics, self-educating business processes, automatic bottleneck detection and elimination, just to name a few.

IoT and CRM

One of the most incredible things about Internet of Things (IoT) is its massive data generation. Gartner estimates more than 21 billion IoT endpoints to be in use by 2020 and according to a report from Cisco’s fourth annual Global Cloud Index study, data from devices connected to the internet will reach 403ZB a year by 2018, up from 113.4ZB a year in 2013. However, in 2016 the majority of data produced by IoT technology was just transient data that moved across networks and wasn’t stored and analyzed.

In 2017, we expect more traction as companies connect new, powerful ecosystems to centralize, manage, and share these massive amounts of data. CRM solutions will be expected to become centralized data repositories that aggregate IoT tech data to drive data-driven decisions. Powered by AI algorithms, IoT sensors will be gathering more information and facts about customers to provide companies deeper insights into client’s physical behaviors. And by analyzing this rich historical data, AI-powered tools tied to IoT infrastructure will enable businesses to improve service and deliver seamless customer experiences offline as well online.

Big Data reinforced with AI

In 2016 we predicted companies would start to realize value from Big Data and while this turned out to be true we expect even more in 2017 as companies double-down on big data to power deep learning. Deep-learning jobs grew from practically zero to 41,000 jobs since 2014 as reported by Gartner.

In 2016, use of big data was varied but the most consistent use cases that we observed were predictive technologies for marketers and business analysts as a way to provide more personalized customer experiences. Research firm International Data Corporation estimated worldwide revenues at nearly $122 billion in 2015 and is expected to hit $187 billion in 2019. By combining several learning algorithms AI-powered solutions provided organizations with the ability to leverage the huge amounts of historical data as well as real-time, behavioral data. With the help of data mining and machine learning methods businesses converted this knowledge to yield more accurate and more actionable models that could predict better content, better offers, and more targeted advertising.

For example, based on historical data systems can help sales or marketing professional to define the sequence of actions that can lead to the most successful outcome with next-best offer or next-best action capabilities. By leveraging historical and behavioral data, AI-powered systems can recommend the best products for up-sell or cross-sell, the most relevant channel to engage the customer, or the most convenient time to provide proactive service.

In a recent marketing tactics survey, Forrester Research asked B2B marketers what were the types of analytics that drove innovation and created new growth for their departments. More than 65% of respondents highlighted performance and predictive analytics as a primary driver of innovation in 2016. Moreover, another Forrester research revealed that 62% of B2B marketers were planning to implement were implementing or expanding their predictive marketing capabilities. 2016 showed that regular employees don’t need advanced degrees in statistics to use the outputs of statistical models for their daily routine. This is likely the biggest factor in the adoption of big data technology – the fact that vendors have been able to deliver value to business users not just data scientists.

Related to business operations and process automation, automated lead scoring that is based on AI and BI algorithms can identify key factors that influence lead maturity and assign the most appropriate weight to each factor. Predictive lead scoring algorithms take historical and behavioral data from the CRM system, and combine that with “big data” attributes gathered from various sources. The method utilizes the collected data to score leads and determine their readiness to be handed off to sales. This helps organizations eliminate manual work as the system can automatically calculate a score based on the likelihood that the lead will convert or it’s likely revenue outcome. This has enabled companies to logically prioritize their sales efforts and focus on working with the warmest leads with the highest score or quality.

These practical and high value benefits of big data will drive more investments in 2017 (and beyond), especially as the technology gets applied to traditional cost centers like customer service. If big data and AI can deliver more relevant front-end experiences, it should certainly be able to improve the post-sale journey. The enthusiasm about chatbots is early indication of this trend.

Conclusion

Looking back at 2016, most of our predictions hit the mark and we expect every one of these areas to gain even more traction in 2017. With the mainstreaming of AI, 2017 should be an exciting year for technical innovation that impacts our businesses (and personal) lives! It’s going to be an exciting year.

?About the Author: Katherine is the CEO and Managing Partner of bpm’online, a leading process-driven CRM for marketing, sales and service on a single platform.