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].