Businesses usually focus on easily quantifiable metrics, such as sales figures or website clicks. Such analysis reveals the popularity and effectiveness of digital initiatives. 

But relying only on such traditional KPIs often does not give the complete picture. A social media post may, for instance, become viral. But it need not translate to sustained sales. What matters for the C-suite is results. As such, there is a thrust on advanced metrics such as cost per acquisition, customer emotions, brand reputation, and so on. These metrics are less quantifiable but help the C-suite get a holistic view and make better strategic decisions. 

Customer acquisition cost (CAC)

Customer acquisition cost (CAC) measures the average cost of acquiring customers. A lower CAC indicates efficient marketing strategies and effective use of digital resources.

 The simple measure of CAC is dividing the total cost of the campaign by the number of new customers acquired within the specific timeframe. The total input costs may include:

• Wages and salaries of marketing, sales, and customer support staff involved in acquisition.
• Software casts, including costs of marketing and sales automation tools, CRM software, and other suites.
• Fees to consultants, agencies, and freelancers who contribute to the campaign. 
• Appropriation of office rent, utilities, and general expenses.

Customer lifetime value (CLTV)

Customer lifetime value (CLTV) predicts a customer’s total revenue over time. CLTV determines the effectiveness of digital strategies in building customer loyalty and driving repeat business.

The simple formula calculates the average purchase value: Divide the total revenue in the specified period by the number of purchases in the same period. The trick is to segment the data or isolate purchases influenced by specific digital campaigns. This might involve filtering the data based on a unique campaign ID assigned to each customer or attaching tags to URLs.

But calculating customer lifecycle value is much more complicated. Two additional variables, average purchase frequency and customer lifespan, make the calculation tricky. The average purchase frequency is the number of times a customer will purchase during the period. The customer lifespan is the average time the customer remains engaged with the business. Identifying these two variables requires historical analysis or industry benchmarks.

CLTV is the average purchase value multiplied by the average purchase frequency and customer lifespan.

The CAC and CLTV make explicit the financial viability of the digital initiative. If a customer’s CLTV is less than the CAC, the digital campaign does not make financial sense, even if it is a roaring success. For simple one-off campaigns, comparing the CAC with the average purchase value will suffice.

Social media engagement 

The social media engagement KPI measures the level of engagement the company receives on its social media platforms. Common metrics include likes, shares, comments, and brand mentions. High engagement indicates a successful social media strategy that resonates with the target audience.

The utility of social media engagement KPI depends on prompt action. The best example is Mondelēz International, the owner of the Oreo. The brand launched an intuitive campaign during the Super Bowl blackout in 2012. When the power went out, the brand tweeted an image of a single Oreo cookie with the text “You can still dunk in the dark.” Over 15 million people saw the tweet, creating a positive buzz for the brand. 

Net promoter score (NPS)

Net promoter score (NPS) is the basic measure of customer loyalty and satisfaction. 

NPS measures how likely the customer will recommend the company to others. A high NPS indicates positive brand perception influenced by digital experiences. A low score indicates failure of the digital outreach. A high NPS is critical in the digital space, where word of mouth and loyal customers matter much more than any advertisement campaign. 

NPS calculation is often through direct feedback, on a scale of one to ten.

Time-to-market (TTM)

Time-to-market (TTM) measures the time it takes to launch a new product or service, from conception to customer. 

A shorter TTM helps the company get products to the market quicker and meet demand well. The longer the time to market, the greater the risk the product will become obsolete or customers will move away.

TTM indicates the business’s ability to respond to customer needs fast and keep pace with demand. In today’s fast-paced world, TTM determines the business’s competitiveness. 

Customer effort score (CES) 

The customer effort score (CES) measures the ease customers attain their goals when engaging with the business. It measures the level of effort the customer requires when making an interaction. A low score indicates seamless processes, whereas a high score indicates some inherent flaws in the system.

Closely related to the CES is the response time, or how fast the brand responds to customer inquiries, requests, or issues. Shorter response times result in low CES and better customer experiences. Longer than acceptable response times confirm some inherent flaws.

 Another related metric is First-Contact Resolution (FCR). FCR evaluates the resolution of customer issues during the initial contact with the company. High FCR rates indicate efficiency and customer satisfaction. Low FGCR rates again indicate inherent flaws.

Emotion analytics

One digital KPI that has the C-suite interested of late is emotional analytics. Emotion analytics provide granular insights into customer emotions. Metrics such as NPS are superficial. Emotions capture a deeper and complete picture of how customers perceive a brand. 

Emotions are difficult to measure. Tools such as voice analysis, facial recognition, and biometric data measure emotional responses. But the challenge is measuring such metrics. 

One approach that has caught on recently is sentiment analysis, which gauges the emotional tone of customer interactions. Sentiment analysis tools analyse data from sources such as social media posts, customer reviews, website chat transcripts, and survey responses. They categorise the customer expressions from these sources as positive, negative, or neutral.

Advanced text analysis tools use natural language processing (NLP). These tools can identify emotions such as joy, anger, sadness, or fear, taking sentiment analysis deeper. But these tools are still under refinement.

Sentiment analysis is not yet a perfect science. It often fails to identify nuances, sarcasm, and even cultural context. Customer feedback forms validate the sentiment analysis.  

Tracking digital KPIs offers the C-suite insights into digital campaign effectiveness. They can use these insights to guide strategic decision-making and optimise investments. Acting on these insights boosts customer satisfaction and boosts business competitiveness.