Tell us about your role and the team/technology you handle at M-Files.

As a VP of Product Marketing, I lead a team that focuses on different product marketing activities at M-Files: product positioning, value proposition and customer communications. My team also employs subject-matter experts for different processes and industries. They work closely with product development, marketing and customer success teams to ensure that we address the specific customer needs for various industries and their respective regulations.

Which Marketing Technologies are you currently following now?

We use and follow almost all marketing technologies from ABM to Sales Automation to analytics. Marketing organizations typically use M-Files for collaboration, data governance, digital asset management, content management, talent management, customer intelligence and workflow.

How do you see Information Management technologies fitting into a modern CMO’s stack?

CMOs can benefit from modern information management technologies in numerous ways. Many marketing organizations often struggle with data being siloed in multiple disconnected repositories that would need to be accessed by external stakeholders. Another common challenge CMO’s face is managing digital assets efficiently and adhering to compliance.

Why are B2B marketing teams steadily moving toward Applied Data Science for Sales and Marketing initiatives? What roles does Document Management and Automation play in this journey?

I believe that this change is driven by the increasing number of regulations like GDPR: marketing messages must be better targeted because customers now have more options to opt-out from mailing lists. Also, the sheer amount of digital marketing collateral customers and prospects are exposed to is ever-increasing and it is harder to create a message that stands out. A good understanding of customers’ past behaviour and preferences may help to keep the messages more relevant to the target audience.

Document management solutions can certainly be used to collect and manage master data for marketing automation and other activities. However, I believe that the biggest benefit of implementing a modern DM solution is the ability to display previously captured data from multiple sources in a single view for a sales team member. This includes lead activities from Marketo, email exchange from Outlook, files from network folders and Dropbox, and customer feedback from customer experience systems.

Tell us more about the changing nature of ECMs and how it affects B2B Commerce initiatives?

ECM software vendors have traditionally invested in full-text search indexing. Some modern vendors have additionally invested in metadata management, artificial intelligence and multi-repository support. The ability to serve the right content from any repository at the right time and in the right context is one of the key success factors for a successful eCommerce platform implementation.

Artificial intelligence can be used not only to gain insights in the product data but also to automate laborious filing and tagging processes.

How do you see Big Data and Customer Intelligence coming together to solve information management-related issues?

Specifically, publicly available big data sources provide some exciting ways to improve targeting of customer communications. One example could be the marketing of B2C products by relying on public data sources such as weather reports or fuel prices and reaching out to customers at the right time. Another similar example could be leveraging real-time traffic data and placing billboard advertisements based on the traffic flow, as consumers might be more receptive to some messages when traffic is congested.

Big data as a definition is of course very broad, yet also a little vague. For some organizations, big data simply means a large volume of data that is received in various formats that needs to be processed fast. This causes challenges that products like M-Files are designed to address with AI-powered intelligent information management solutions that can retrieve data from multiple business systems and file repositories.

Tell us about the various steps of the AI-powered ECM journey.

Probably the lowest hanging fruit for AI-powered use cases for ECM might be the automation of routines within a system. Users spend a lot of time filing (and mis-filing) content in these systems. This work is also de-motivating and monotonous for a user. This process is often relatively easy to automate at least partly and these implementations are great investments to achieve end-user satisfaction and higher engagement with a system.

After the mundane routines are automated, it is valuable to look at the digital content landscape in the enterprise. It is estimated that more than 50 percent of digital data in enterprises is “dark.” That means that the data is captured digitally but it cannot be used to support decision making. It is easy to overwhelm users with too much data and therefore the focus should be shifted to promote only relevant and applicable content to users when they need it and always in the right context. AI can be used to discover business critical information from the “ROT” (redundant, obsolete, or trivial) data and the ability to make this distinction can significantly increase efficiency of the processes and workflows.

Finally, AI can be used to discover such insights from data that are too time intensive to uncover via manual processes. At M-Files, we recently worked with a customer that had 10 million files on a network folder and needed to identify all files that potentially contained sensitive customer data. We ran our analysis and found about 20,000 records. This exercise could not have been done without a modern text analytics toolset.

What are the different scenarios AI could be applied to in Marketing, Sales and Customer Service?

There are multiple use cases. Some popular ones among our customers have been the following:

• Using computer vision to identify objects in pictures. This helps to automate indexing of stock photos and reduces the marketing costs, as purchased content can be reutilized more efficiently.
• Using text analytics to identify PII data. This is relevant, for example, to maintain compliance with GDPR and other data regulations. Marketing teams often store account export lists from marketing automation systems to network folders and this is a good example of a GDPR violation.
• Applying sentiment analysis on customer feedback through email and social media. This helps customer success organizations to prioritize support cases and on the other hand helps marketing teams to manage testimonials more efficiently.

Tell us about your customer experience products. How can MarTech customers benefit from AI/ML applications?

The examples provided above are great examples of use cases for M-Files. Others include automating data classification and governance with text analytics, for example.

What are the major pain points for Product Management and Innovation teams in building/ scaling customer experience?

My experience is that customers no longer buy software or cloud services. They are looking for truly valuable business solutions with fast time-to-value. A customer seeking a contract management solution, for example, is no longer interested in technology and features only: they want to improve their business processes and want to work with (cloud) vendors that can guide them with best practices and ways to mitigate business risks, measure efficiency gains and the like.

From a product management point of view this requires hiring employees with diverse expertise (technologists as well as subject-matter experts). It also makes managing product roadmaps and responding to customer demands more challenges because the development focus will have to shift from developing generic features to providing solutions for very specific business problems.

One advice to all Marketing and Sales technology vendors and customers for 2019-2020 –

For vendors, establishing customer success teams, user communities, and advisory groups should be an important focus if you do not have those yet. Your customers have the most insight on solving the solutions they buy SaaS products or software for, so it makes sense to listen to them carefully.

For customers, do not try to solve all the problems in the world with your marketing and sales solution. With SaaS solutions especially, it is cost-efficient to start small and focus on quick wins and low hanging fruits. This can often be done with less customization and with smaller project budgets. Often, the ROI of the first phase implementations is typically the highest. Many vendors provide solution templates that provide a great, cost-efficient starting point.