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The marketing team primarily

A non-marketing team primarily (e.g. IT or product)

The marketing team, in collaboration with several different teams like engineering and analytics

Cross-functional digital product team(s)

Never

Once or twice a year

Once a quarter

Once a month

Bi-Weekly

Once a week

Several times a week

Continuously/non-stop

Ad hoc A/B testing to measure comparative results

Multivariate testing with granular customer segments (e.g., location, time of day) 

Multiple concurrent experiments using advanced analytics to gain in-depth insights 

Ongoing partnership with business intelligence to ensure tests are statistically significant

We don’t follow a specific experimentation framework

Mostly team-focused, with each team or department evaluated for its part 

A mix, with a single company-wide definition of success, split out for each team’s role

Company-wide, with a single definition of cross-functional success across all teams

Message engagement, like open rates click-through rates

Enrollment, subscriptions, or message opt-in/outs

App engagement and usage

Volume of messages sent 

Revenue per campaign

Product adoption

Customer retention

Customer acquisition costs

Customer satisfaction scores

Customer lifetime value

Operational expenditure

Other 

We don’t use KPIs/metrics to measure customer engagement success

Boost high-level engagement metrics (e.g. message open rates and click-through rates) 

Enhance a mix of engagement metrics (e.g. opens) and downstream metrics (e.g. monetization)

Combine engagement/downstream metrics with mapping customer behavior/sentiment 

Combine engagement/downstream metrics, customer behavior/sentiment, and  company/product strategy

Zero-party data (e.g. preferences) is combined with first-party data (e.g. digital behaviors)

Customer data is augmented with third-party data (e.g. location, weather)

Modeling (e.g. predictive purchase modeling) is used to generate additional data

We combine data from different sources into a single customer profile 

Other 

We don’t combine data from different sources

Sorted based on the channels in which customers selected to receive messages 

Sorted using customer information and past behavior or engagement 

Sorted on a real-time basis as new inputs are collected

Mostly via one primary messaging channel 

Multiple channels that each have channel-specific solutions (e.g. email service providers, push providers)

Multiple channels, each with campaigns planned and managed manually via a single interface 

Multiple channels orchestrated across campaigns/customer lifecycle stages and managed via a single interface

Different solutions for each channel, including vendor-provided tools or in-house solutions 

A single solution that executes across all channels 

A single solution that automatically optimizes which channels are used at the customer level

Customer-provided account information, like birthdays and names 

Past customer behavior, like viewed or purchased items

Past customer engagement with your digital properties (e.g. browsing behavior, clicks)

Customer context from internal systems or public APIs (e.g. weather) for content personalization

Real-time engagement with your digital properties (e.g. browsing behavior, clicks)

Send-time optimization to determine the best time and cadence to send a message

We don’t personalize messages

We review the results (e.g. engagement, revenue) before starting the next campaign

We use advanced tools to determine efficient or high-value successes

We sync performance feedback with insights generated by other platforms

Our performance feedback automatically adjusts campaigns in progress

We export performance feedback to business intelligence platforms to generate advanced analytics

We don’t measure the performance of our campaigns