What is the future of CRM analytics? What upcoming problems might exist? What aspects might be in decline?

 

Source: (Playbuzz.com, 2016)

So what is the future of CRM?

In the past, the meaning of CRM for everyone was sales force automation only. Then, as things were moving forward, marketing automation was added to that functionality. All that wasn’t there was actually social channel incorporation only.

Before we speak about the future, let’s take a look at what we have in hand right now.

At the moment, CRM is mostly focused on internal business management practices such as customer information management, market strategy design (based on sales) and basic customer related communications. They are designed to increase process efficiency and reduce operational cost by analyzing sales and in turn influencing business operations. But somehow, organizations still fail to inspire customers enough to improve and retain relationships. With social media advancements, a communication evolution followed. This communication evolution is going to lead us to the future.

In the future, with social CRM will help organizations be better customer oriented. The future goal of CRM will be to improve communication between an organization and its customers. It will be an application to know and explore consumer needs. In a recent case study sponsored by SAP on six innovative companies, namely e de Transport de Montreal, Lenovo, Australia and New Zealand Banking Group, ARI, coop@home and CEMEX, studies revealed that “Businesses need to provide an unbroken and highly relevant conversation across channels, responding to and even anticipating customers’ ever- evolving needs. In today’s terms, this is known as ‘customer engagement’.” (SAP, 2015) This shows a changing trend in needs of the customers. Better customer engagement will also help build tailored strategies that each customer would desire. Speaking of customer engagement, it is exactly where CRM is headed towards.

In terms of data science, the biggest difference will be changes in research objectives. Instead of concentrating just on sales data, consumer behavior and engagement will be the new areas of concentration. For example, a future CRM system would be able to discover customer demands based on onsite behavior or targeted personalized recommendations and promotions.

 

Source: (act-on.com, 2016)

Upcoming Challenges to CRM and data science

The future will challenge data science for better or for worse in two major ways being, data and technologies.

Scalability

Scalability will continue to challenge data analysts. With more customer interaction, data volumes are at an all-time high and will still continue to increase. Although, CRM has transferred from sales driven to a customer centric model, strong data analysis plays important role to provide insights about customers. “During the next eight years, the amount of digital data produced will exceed 40 zettabytes, which is the equivalent of 5,200 GB of data for every man, woman and child on Earth, according to an updated Digital Universe study released today.” (Mearian, 2012)

Technology

The technological challenges that exist in data science will hinder CRM transition as well, especially dealing with poor quality data. Large and noisy datasets will definitely add to difficulties. Also, having an omni-channel platform presence definitely helps organizations engage with their consumers but it adds to difficulties in managing consumer data and getting better insights from it.

All in all, if we think about what might be in decline when talking about the future of CRM and data science, then the first and last thing that comes to mind is non-customer oriented traditional practices. This is because, all the decisions that CRM will help aid stakeholders in making can be data driven and concrete. 

Interesting reads for the literary inclined geeks and references

• Cdn.playbuzz.com. (2016). Playbuzz.com. [online] Available at: http://cdn.playbuzz.com/cdn/c9c1b883-9a63-4467-8830-b083f322b786/b1853fe9-7972-4fff-8740-dfb8ea7c3213_560_420.jpg [Accessed 13 Jun. 2016].
• Evergage. (2014). The CRM of the Future: Data Curation and Predictive Analytics. [online] Available at: http://www.evergage.com/blog/crm-future-data-curation-and-predictive-analytics/ [Accessed 13 Jun. 2016].
• Forbes.com. (2015). Forbes Insights: Customer Engagement. [online] Available at: http://www.forbes.com/forbesinsights/sap_customer_engagement/index.html [Accessed 11 Jun. 2016].
• Act-on.com. (2015). [online] Available at: https://www.act-on.com/blog/wp-content/uploads/2015/03/Customer-Engagement.jpg [Accessed 13 Jun. 2016].
• French, T., LaBerge, L. and Magill, P. (2011). We’re all marketers now. [online] McKinsey & Company. Available at: http://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/were-all-marketers-now [Accessed 12 Jun. 2016].
• Mearian, L. (2012). By 2020, there will be 5,200 GB of data for every person on Earth. [online] Computerworld. Available at: http://www.computerworld.com/article/2493701/data-center/by-2020--there-will-be-5-200-gb-of-data-for-every-person-on-earth.html [Accessed 12 Jun. 2016].
• Schaeffer, C. (2016). The Future of CRM Software. [online] Crmsearch.com. Available at: http://www.crmsearch.com/future-of-crm.php [Accessed 13 Jun. 2016].

Ongoing trends and techniques in CRM analytics and the challenges faced

 

 

Source: (raconteur.net, 2016)

Ongoing trends and cutting-edge techniques in CRM software

Now that we know what CRM software is and what it is capable of, let’s take a deeper look into ongoing trends in the market and the cutting-edge technology that is being used to maximise CRM software’s potential.

Before we go any further, let me briefly introduce an ongoing CRM related trend floating in the market these days. Big data CRM – As the name suggests, Big Data CRM basically refers to the practise of combining big data with a company’s CRM processes. This essentially means that the software uses data that exists outside of the ecosystem of the company, such as social media etc. It adds to the value of older CRM practises by finding patterns in this data that can be used to exploit newer sales opportunities and help in adjustment of existent product and service offerings.

“Combining big data with other CRM data can improve customer analysis and lead to predictive modeling and other practices.” (Rouse, 2015)

Ever since the ‘big-data’ buzzwords have been attached with CRM, cutting-edge technology has been aligned with this type software to maximise its potential and deliver the best possible results.

“CRM analytics can range from being as simple as query and reporting capabilities to sophisticated data mining and predictive modeling over large quantities of data.” (Schantz, 2010)

Key success factors of successful CRM analytics is based on strong data warehousing capabilities including consolidation, cleaning and formatting and also on ALL customer touch points to create a holistic view of the customer and their purchasing habits.

From multi-channel to omni-channel -

In order to keep up with changing trends, companies have now created their presence on social media, SMS, video and live chat etc and other platforms to keep in touch with their customers.

Big data CRM analytics software helps keep the stakeholders informed of customer decisions and interaction patterns in order to maximise efficiency.

Challenges faced in using cutting-edge techniques and technologies

Admittedly, big data provides competitive and intelligent insights on customers. With the increases in data size, larger data noise became a new challenge for CRM analytics to handle. For instance, if I use my Amazon account to buy a couple of fiction books for my friend, then, I start to receive recommendations like other fiction books and movies. But it was only a time transaction that I am making. And actually, if it weren’t for her, I would not purchase any fictions for myself. Or maybe, I have 3 different accounts on Ebay.com and I am making different types of transactions from different accounts. These scenarios definitely add to difficulties in CRM analytics.

We enjoy the flexibility brought by omnichannel applications and ofcourse cloud solutions.  But, although the external picture with using omnichannels is wonderful, data transformation and integration from different platforms is hard for CRM companies. Customer’s usage of an application changes from one channel to another also at times. Multi data silos makes the process much more complicated than it already is. These channel changes are not limited to devices. Some of the business might includes social media and video chat. Some might supports four or five channels. The burden is heavier for analytics to collect and process data.

There will always be challenges and limitations on a cutting-edge technology. But people always find solutions to cope with issues. Similarly, data scientists will work out solutions for big data CRM analytics and bring better results to the table for their clients.

Just to keep you geeks up to date with the latest technological trends in CRM: IoT cloud and Marketing cloud integration

“A Microsoft-Salesforce partnership focused on interoperability between the two companies' products signals the push to make device data available and actionable.” (Ehrens, 2015)

This would help stakeholders gain better insights and aligns with the above mentioned success factor of organisations taking note of ALL customer touch points.

Interesting reads for the literary inclined geeks and references

• Ehrens, T. (2015). Emerging CRM technologies took center stage in 2015. [online] SearchCRM. Available at: http://searchcrm.techtarget.com/news/4500269181/Emerging-CRM-technologies-took-center-stage-in-2015 [Accessed 4 Jun. 2016].
• Milletti, U. (2015). The Enemy of Data Science: Noisy Signals in the Enterprise. [online] 1to1media. Available at: http://www.1to1media.com/view.aspx?docid=35392 [Accessed 4 Jun. 2016].
• Nicastro, D. (2014). Mobile, Social, Big Data Create 'Perfect CRM Storm'. [online] CMSWire.com. Available at: http://www.cmswire.com/cms/customer-experience/mobile-social-big-data-create-perfect-crm-storm-025811.php [Accessed 4 Jun. 2016].
• raconteur.net, (2016). Landing image. [image] Available at: http://raconteur.net/public/img/articles/2013/09/Report-Landing-Page-Main-Image-Big-Data.jpg [Accessed 5 Jun. 2016].
• Rouse, M. (2015). What is big data CRM (big data customer relationship management)? - Definition from WhatIs.com. [online] SearchCRM. Available at: http://searchcrm.techtarget.com/definition/big-data-CRM-big-data-customer-relationship-management [Accessed 4 Jun. 2016].
• Schantz, N. (2010). The difference between CRM analytics and traditional data mining. [online] SearchCRM. Available at: http://searchcrm.techtarget.com/answer/The-difference-between-CRM-analytics-and-traditional-data-mining [Accessed 4 Jun. 2016].
• Smith, A. (2016). Multimedia-based multichannel CRM still faces uphill battle. [online] SearchCRM. Available at: http://searchcrm.techtarget.com/feature/Multimedia-based-multichannel-CRM-still-faces-uphill-battle [Accessed 4 Jun. 2016].