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If your organization has customer data, collected through various sources, it can gain insights into what those customers want. Take the data analysis further, and you can improve customer engagement.
These six practices help marketers convert customer data into knowledge that drives higher engagement. A business can use the information gleaned from data analytics to improve customer engagement strategies, even to formulate a new strategy. Data-driven marketers can also learn which analytics methods to use -- conversations, behavioral data and more -- that will strengthen customer engagement.
1. Develop or improve upon the strategic vision
Before you begin analyzing customer data, create a clear mission statement about the core purpose, and include why your organization exists. This statement is best made in the present tense. The mission statement should be followed by a vision statement that identifies the desired future state. The vision statement should guide the organization and delineate its guiding principles. With a mission and vision statement, you can set a strategic vision that uses these statements as guardrails to measure customer engagement.
A strategic plan should serve as the foundation for how an organization approaches and designs its customer engagement strategy, and the role that data analytics plays. One approach is the Six Sigma concept of define, measure, analyze, improve, control (DMAIC) to improve engagement. DMAIC includes the following actions:
- Define customer engagement as a problem or hypothesis of what you believe is happening.
- Identify stakeholders and get their vision for customer engagement and what they believe the challenges are.
- Identify who can help define the range and scope of analysis.
- Consider what data you have versus what data you need to identify changes in the engagement over time, and where the gaps exist. Some good sources of information include:
- business intelligence reports;
- web reporting and analytics;
- analytics from customer support teams for behavioral information;
- predictive analytics; and
- text or sentiment analytics from open-ended survey questions and social media.
- Measure relevant customer engagement data and conduct basic analysis to spot gaps and outliers.
- Analyze the data for correlations and patterns. This step requires statistics and visualization skills.
- Improve based on insights showing several options to explore.
- Control the change by deploying A/B tests. Use these tests, where there is one control group and one group experiencing new content launched on your website, with monitoring KPIs. Key performance indicators can include:
- Conversion rate. Conversion can mean, for example, when a customer downloads a white paper from the website, signs up for email newsletters, registers for a free product trial, clicks on your ad on social media or visits the pricing page for a product.
- Pages per session, which is how many pages the user visited on your website.
- Net promoter score (NPS), which is a measure of customer experience centered on the likelihood to recommend.
- Session time, or how long the customer spent on your website.
2. Make the most of customer data analytics
You don't need to overhaul everything to do analytics in order to build customer engagement. Start with what you have. Mine through and connect all available data sources to gain insights into customers, including where new data is needed to make the customer engagement picture complete. To make the most of the data, you must measure the right things.
Develop a clear understanding of the available data to ensure it is appropriate for use related to customer engagement. There is no one-size-fits-all option when it comes to data and analytics for customer engagement. To get to the right data, ask these questions:
- Who is the data about?
- Where is the data measured in the organization?
- What data is measured? What is the purpose of its collection?
- How is the information used? The action could be decision-making, stakeholder status updates or customer performance dashboards.
3. Segment customers with personalization
Customers can be grouped into segments based on common attributes. Segmenting them in this way enables marketers to seek in-depth understanding of potential behaviors and impacts, e.g., positive and negative and growth opportunities. Identifying segments greatly enhances marketers' knowledge of how those customers behave, informing an optimization plan.
Data commonly used for segmentation is descriptive, such as demographics and lifestyle, career details and industry. This information aids in understanding habits and intent. It is appropriate to use to predict buying habits, personalize communications and ultimately identify customer segments accurately. Segments can be validated via customer interviews or qualitative means, such as focus groups.
An organization can turn these core segments into customer, buyer or user personas and determine personalization approaches and methods most appropriate for and appealing to each one.
4. Craft effective internal and external conversations
Organizations that are transparent with their employees and how they operate, with limited silos, want to engage. This approach extends to open communication between the business and analytics leaders.
Customer insights gleaned from collected data and feedback can be the catalyst to bring teams together. To drive change and take action, companies must use the feedback from these conversations.
Internal and external conversations are two recommended approaches:
- Internal conversations are driven by the learning provided by customers with stakeholders. An internal conversation can act as a follow-up to customer feedback. It can provide additional insight and marketers can take recommendations from the insights and turn them into actionable next steps.
- External conversations occur with both customers and partners. These conversations serve to pull back the initial learning layers to gain additional insight from customers, and test out potential engagement approaches.
5. Predict customer actions
An organization cannot accurately predict customers' actions, such as buying or consumption behaviors, unless they continually learn about them. Behavioral data refers to general patterns that customers perform when interacting with your business. It clarifies customer patterns when interacting with the company or brand, related to online browsing activity. Behavioral data includes social media use and consumption as well as site-related data points such as pages viewed and emails opened.
Using website analytics such as those identified above can help marketers understand how customers feel about the company's products and services.
The keywords customers use to find your website, the device they use to view it, and the amount of time they spend on the site can help steer choices to optimize the customer's journey. This effort can generate more conversions.
The data shows which channels attract the most conversions, so you can spend on marketing where it's most effective.
Use customer feedback such as online surveys to gain insights on customer attitudes and sentiment, opinions, preferences and motivations. This data also shows which channels generate the most conversations.
Collecting and analyzing customer data not only improves marketing campaigns. It also enables you to elevate aspects of the customer experience, such as product recommendations, communications, retention and loyalty programs.
6. Understand the business
Every business is different and should tailor its use of data for a customer experience program accordingly.
An organization that has a brick-and-mortar store may approach data differently from an e-commerce site. Similarly, the strategy can look different if customers are consumers, versus a B2B transaction or business-to-business-to-consumer companies. Use the data points you collect to understand where and how customers engage with the company, and then implement actions to get customers to better and faster decisions.