Assuring the Omnichannel Experience
The key to keep customers happy and loyal is knowing your customers, understanding what they want and being able to deliver.
According to a recent survey by Forrester, 90% of enterprises prioritize to improve customer experience with their brand.
As customers navigate toward their goals, they may use a variety of channels, including phone, email, website, chat, text, chatbot, IVR, mobile apps, and others. Moreover, they expect their journeys across multiple channels and functions to be simple and seamless.
To meet customers’ lofty expectations, organizations should be investing in Omnichannel Analytics, so they can:
- Understand and properly engage each customer
- Predict customer behaviors and design the right journeys
- Personalize resolutions and recommendations
- Optimize contact center staffing and processes
What’s Behind Omnichannel Struggle?
Organizations may not have the right tools to measure and evaluate non-phone interactions. Most do not have tools to monitor customer interactions across multiple channels. The top five challenges inherent when implementing the omni approach are easily overcome
Data Overload: Volume Keeps Going Up
Corporate contact centers have been recording, monitoring and measuring customer phone calls for years. Even with the many advances in software, nearly half (48%) of contact centers admit to collecting and reporting on metrics that they don’t use.
The sheer volume of data generated by omnichannel interactions requires an automated and intelligent system for data capture and storage.
TIP: Invest in an omnichannel analytics platform the can ingest and aggregate disparate data from sources anywhere in the organization. Don’t add to the complexity of the data. Any information that has some kind of customer identifier and a time-stamp associated with it can ultimately be sequenced into a customer journey.
Data Quality: Truth and Accuracy
Omnichannel data aggregation is a challenge for many organizations, and manual processes are not only time-consuming. Human error could affect data accuracy as well. That’s why organizations use Artificial Intelligence (AI) to sift through the data.
Another challenge to obtaining quality data is to identify the metrics that are relevant for each channel, and what constitutes a good experience versus a bad experience. For example, the primary goal of a chatbot may be for customers to complete tasks easily and independently, not necessarily quickly.
TIP: Rethink the metrics you need to use to evaluate each channel and choose an omnichannel analytics platform that can aggregate 100% of your data, both structured and unstructured, from your contact center, IVR channel, CRM, webpage, etc . This will enable you to get a full consolidated view of customers and to understand the who, what and how of the journeys they are taking with your organization.
Data Integration: Connecting the Silos
Raw data on how customers are interacting with your organization typically scattered around different places. Bringing all data together and managing it around is a significant challenge. Journey analytics is based on hard data that represents 100% of interactions across the organization. It includes the silent majority of customers who will not fill out survey forms or be involved in interviews.
Without intelligent data integration, organizations are left with too much data and not enough clarity on how to use it.
TIP: Deploy an omnichannel analytics solution that can bring together quantitative and qualitative data from disparate sources, and then connect, stitch and weave the data into customer journeys that let you cut through the noise and focus on getting the insights that are critical to your business.
Lack of Analytics Expertise
Even if they can connect all the disparate data sources that describe how customers are interacting with the organization, they still need very smart and often quite expensive people to process, analyze and derive insights and actionable intelligence from the data.
TIP: Choose an omnichannel analytics system that uses AI to do the heavy lifting of normalizing, sorting and correlating cross-channel data so your employees don’t have to. Also consider using analyst services to help personnel ramp up and become proficient at using analytics dashboards, templates, report generators, etc.
Hard to Get Management Buy-In
The C-suite wants to see results. To get their buy-in for allocating time and resources, omnichannel analytics champions need to demonstrate value, set realistic expectations and avoid the urge to “oversell” the project. Also, top-level managers must see the value in making far-reaching changes in their business based on the insights gleaned from analytics.
TIP: Choose an omnichannel analytics solution that lets you start with a limited project that can produce fast and compelling results. Keep it simple and focused. Success depends on how easy it is to discover actionable insights that translate into measurable value for the organization.