Companies are swimming in data. The amount of data that is collected on customers is not slowing down. On top of that, every business function wants data to answer questions about the customer experience or function they specifically own.
More companies are seeing their business functions operate independently in collecting data and analyzing it. Oftentimes teams create their own surveys or look at only a subset of calls, chats, social posts, or any other customer communications. This process is dividing data and does not always give a good representation of the customer experience. Disparate data and teams create disparate customer experiences. Enter Enterprise CX Analytics (Khoros CX Insights).
Enterprise CX Analytics is not being discussed enough by businesses because most are not aware of the concept, or don’t understand the soft and hard benefits it can provide. The concept of bringing together all your company’s customer data into a singular analytical view is the best way to analyze the customer experience. Customers do not view a company as a siloed entity – they view it as a singular brand.
Enterprise CX Analytics goes beyond your normal data lake of structured customer data – a truly Enterprise CX Analytics platform includes all the unstructured data, including customer conversations. Unstructured language data makes up more than 70% of all data in the world, and that number is growing every day*. Every interaction with or about your company, regardless of where it happens, should be analyzed. Structured data is extremely valuable; however, nothing provides more insight into how customers experience your brand than conversations. Having all these conversations in one place in conjunction with structured data will greatly help any CX program.
The impact of Enterprise CX Analytics cannot be understated. Below are five of the most profound benefits of implementing Enterprise CX Analytics into a company:
1. Single Source of Truth (Omnichannel) – The greatest benefit Enterprise CX Analytics has to offer is the way it helps companies overcome the inefficiencies with data being scattered across multiple business functions. Collecting all your customers’ data is a challenging feat due to the uniqueness of all the data points you must collect; however, similar to an omnichannel experience, data needs to be connected. Establishing a data foundation that joins data where possible helps ensure a business is looking at the experiences they offer with a consistent lens. With a unified dataset, customers can be viewed holistically rather than based on the channel or platform they use to interact with a brand. No more arguments about which data source is more accurate.
2. Collaboration – Breaking down data silos inherently breaks down business silos and forces teams to work together. Without silos, work no longer gets shelved between the cracks or kicked around to different owners. Having a single source of truth expedites the entire data collection and customer analysis process. Business functions can begin collaborating sooner on projects, and with a holistic view of the customer, can better understand how their work impacts other teams. Without an Enterprise CX Analytics platform, teams often have little to no insight into how their work affects other functions. Customers often voice how their experiences are disjointed and broken, but with an Enterprise CX Analytics platform, those poor experiences are much easier to tackle, together.
3. Prioritization – Companies prioritize their work based on the information customers provide; however, if that information is not complete the wrong decision can easily be made. If you are missing conversations or data points from your customers you could be missing your next multi-million dollar priority. Enterprise CX Analytics gives business functions the ability to compare their priorities against the priorities of their customer, and because there is a single source of truth, prioritization is no longer based on a myopic survey or a single piece of feedback. Arguments about which initiative needs to be done next can be compared side-by-side with exactly what the customer wants most.
4. Natural Language Processing (NLP) – The greatest analytical technology in the last few years has been the accelerated improvements of Natural Language Processing. The ability to analyze conversational data is not a new capability; however, it was only recently that the technology became accurate enough and accessible enough for business to use at scale.
5. Actionable Information - The most important aspect of any data or insight is how impactful it can be on the business. Providing an extensive description on the end to end experience, and the cause and effect of each action throughout, creates actionable information. Most data provides the results of an experience, actionable data details what is driving those results.
The benefits of Enterprise CX Analytics are undeniable. Few analytics solutions can inherently force a company to be better at CX. The five items listed above don’t require a lot of work from engineers or IT, they are the byproduct of having a best-in-class unified platform, like Khoros CX Insights (formerly Topbox), and being customer-obsessed.
If you found this post valuable, I encourage you to watch our webinar “Why CX Programs Struggle to Demonstrate Results.”
In this webinar, I’ll be discussing the three common obstacles to proving the business results of your CX program and how to overcome them.
*Siegel, Eric (2013) Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die
Excellent article @CStark . I really like the stats about unstructured data. I would go so far as to say that you can't build a connected, omnichannel customer experience if you first don't have connected, omnichannel data!
Totally agree with @JacobBo! The stats make a compelling case for how valuable CXI is to our customers and emphasize what it can do for prospects.
Putting this into some outreach - I'll keep you posted.
Spot on @CStark! Its all about governed data.
The ability to summarize unstructured text AND associate it to business metadata across channels is dependent on effective date normalization. Most conversation solutions have different metadata and conversation styles. Brands unfortunately do not get out of the box decoder rings that make it easy to unify the data formats without costly custom solutions to unify disparate data.
Effective Insight generation approaches demand organic and evergreen classification models that can be applied in iteration. Without a common data set, this is just not possible. Governing cross channel conversation data is largely cost prohibitive without a AI backed unification service like Khoros CX Insights to do the heavy lifting.
Love this article @CStark! Agreed that the stats around unstructured data are monumental and truly put us above other solutions. When pitching CXI and mentioning unstructured data to both prospects and customers, in your opinion is the best way to frame this by saying that CXI can find all the intangibles not stored in database format like human speech through phone and content within videos? Curious to hear what's resonated best with prospects/customers as we know this is new to a lot of them.
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