By Andrew Davies, idio
Many businesses are working hard to become customer-centric by implementing programs dedicated to collecting and responding to customer interactions and feedback.
Businesses that do this well are not only more likely to improve the loyalty of their customers, but to reap significant financial benefits. Aberdeen Group’s report, Voice Of The Customer: Big Data As A Strategic Advantage, noted that businesses with a robust customer-centric initiative in place expect to increase annual revenue by 10.9%.
The strength of such programs relies on the customer information recorded and held in the customer database. The CRM software revolution has meant that previously disparate customer data — their contact details, firmographic data and product purchase history — are now (hopefully) structured: neatly captured and standardized in rows of the customer database. This has had a profound effect in sales, marketing and service environments where reps have been able to leverage this information to engage with customers in increasingly relevant ways.
Yet despite this progress, the majority of customer insight continues to lie outside of customer relationship management tools in the form of unstructured data. Examples of unstructured data include your emails, customer feedback reports, call center logs, ordering information, surveys, social media, and blog posts — and importantly, how people engage with each of these. It is vital information that can yield detailed insight about their digital body language — interests and intent — yet it lies outside of the CRM.
It’s not an issue that has escaped Marc Benioff, CEO of Salesforce. Last year at DreamForce, he declared that unstructured data now outweighs structured data 5-to-1 within the marketing, sales and service environments. Benioff went on to identify that the biggest imperative for customer-centric companies is to make sense of their unstructured data so as to be in a position to leverage it to better understand and influence the customer journey.
Making Order Out Of Chaos
Content volumes are going through the roof, according to Benioff: “90% of the world’s data was created in the last two years…There’s going to be 10 times more mobile data by 2020, 19 times more unstructured data, and 50 times more product data by 2020.”
There is no way that a manual approach will keep up with these volumes. The process is best turned over to machines, using a Natural Language Processing (NLP) approach. NLP refers to a type of content analytics that can be applied to unstructured content to “read,” parse and structure it. This processing identifies the ‘topics’ that are contained with each piece of content (i.e. people, places, products, model numbers, etc.) and adds them as descriptive metadata making it easier to understand at a glance what the key themes within each piece of content is without having to pore over it yourself.
Regardless of whether a piece of content is a call center log, a salesperson’s note, an email or a transcript, by extracting the content topics in each content item we can turn what was previously unstructured into structured content. It is from here that customer-centric organizations are then in a position to use the data to derive actionable insights about their customers.
Putting Unstructured Data To Work
Every customer interaction in your business produces unstructured data that can be mined for insight and business intelligence. Here are some examples:
- Call center transcripts can be analyzed to identify recurring themes (negative or positive) or product issues;
- Social media can help identify the topics which resonate and affect your most passionate advocates; and
- Tracking the most engaged readers of your email program will reveal the subjects that are most engaging to them and can be applied to your content marketing in other channels.
A little closer to home, we see clients unlocking customer insight from their unstructured data in a variety of ways.
One digital publisher uses it to understand the emerging context of every reader based on their reading arc. As the reader consumes more online content, the publisher’s database collects the evolving content topics of interest. This enable the sales and marketing team to present each reader with the right commercial offer based on their known current interests.
Another example is a major U.S. car manufacturer that is learning which of their large audience is likely to be in the market for their new upmarket sedan, before its even launched, by identifying and communicating with those showing a fit of interests and purchase propensity.
A leading charity is able to understand from analyzing content consumption on its blog posts which patrons are more interested in “water sanitation treatment” and “Lima,” and which are more interested in “Romania” and “orphanages.” These topics are made available in their Salesforce CRM tool so that the call center team can target current and future donors based on what they’ve been reading.
Unstructured data is a potential goldmine for businesses. By harnessing unstructured data, organizations will be able to make better products; give a better service; provide more relevant and timely communications and even predict what consumers want, before they even know they need it. The companies that are getting it right, by listening in the right places, using this new insight to augment structured data, and taking action accordingly, are already starting to build a huge advantage over their competitors. In an increasingly flat and competitive world, understanding the digital body language of your prospects and customers really matters.
Andrew Davies is the CMO and Co-Founder of idio, and helps leading content marketers maximise the value of their content marketing. idio’s Content Intelligence platform analyzes your content automatically, understands your customers via the content they consume, and recommends the right content to the right person in real-time, on any channel. Follow Andrew on Twitter @andjdavies.