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Generosity Marketing Part IV: Using Optimove’s AI to Optimize Generosity at Scale

See how using AI powers generosity marketing to maximize engagement, customer lifetime value (CLTV), and loyalty

Re-engage your churned customers with this guide

Why it Matters:

In the first part of this series, we described the building blocks of optimizing generosity and how marketers can evolve from “too generous” to “generous enough” in marketing. The other parts of this series are on the following building blocks to create an effective system to optimize generosity:

1. Build a strong data foundation

2. Have clean testing frameworks

3. Use Optimove’s AI to optimize generosity at scale  

This final post is on #3. After building a strong data foundation and clean testing frameworks, use Optimove’s AI to optimize generosity at scale.

Key Takeaways:

Optimove’s AI optimizes generosity at scale with the following:

  • Self-Optimizing Journeys use AI to choose the most impactful campaign for each customer among all eligible campaigns to optimize customer lifetime value.
  • Self-Optimizing Campaigns optimize campaign performance using AI to autonomously determine the best offer for each customer.
  • Optibot provides one-click actionable recommendations, such as dropping underperforming campaigns, identifying anomalies in data, or recommending campaigns with a positive impact on customer LTV. It also focuses on segments to better cater to customers’ responses, helping maximize the effect of a brand’s marketing efforts.

Step #1: AI to choose the most impactful campaign with Self-Optimizing Journeys (SOJ)

Marketers no longer have to rely on intuition, experience, or a strategic priority to decide what campaign to send at each step of the customer journey. Optimove’s Self-Optimizing Journeys use AI to choose the most impactful campaign for each customer among all eligible campaigns to optimize customer lifetime value. It makes creating the most personalized journey for each individual customer possible.

With Self-Optimizing Journeys, marketers can finally provide customers with journeys that adapt to each one’s unique characteristics and behaviors, effectively allowing customers to lead their own journeys.

Self-Optimizing Journeys identify all the campaigns each customer is eligible for and evaluate all journey possibilities, response probabilities, and potential impact on customer lifetime value, to determine and serve the next-best campaign for each customer.

Optimove research shows that marketers who use Self-Optimizing Journeys to provide their customers with personalized customer journeys achieve high impact:

  • 37.4x higher incremental uplift per customer when compared to manually prioritized campaigns
  • 46% increase in total incremental uplift for brands with over 20% of total campaigns included in Self-Optimizing Journeys when compared to manually prioritized campaigns
  • 450,454 weekly average autonomous decisions made

Read more about Self-Optimizing Journeys here.

How to prevent churn and reactivate customer

Step #2: Optimize campaign performance with Self-Optimizing Campaigns (SOC)

Self-Optimizing Campaigns optimize campaign performance using AI to autonomously determine the best offer for each customer. Optimove’s SOC is a particular type of A/B/n campaign that’s comprised of two+ competing actions plus a control group for comparison. It learns and improves campaigns automatically via its results over time and reduces guesswork around choosing the right campaign or creative by sending the optimal message to each audience member.

This innovative use of AI goes even further than that. Marketers say goodbye to using a winner-takes-all approach when it comes to A/B testing. Using AI, marketers can run A/B/n tests that automatically deliver the treatment that each customer is most likely to respond to.

For example, SOCs can maximize profit margins by optimizing discounts to customers based on their discount affinity. It will even send no promotion at all to customers who will pay full price.

Get deeper insights on Self-Optimizing Campaigns here.

Watch: How Over-Generous Marketing Stops Businesses Getting the Most from Customers

Step #3: Optimize even further with – Optibot

Once campaigns are running in an optimized journey – Optibot provides one-click actionable recommendations, such as dropping under-performing campaigns, identifying anomalies in your data or campaigns with a positive impact on customer LTV, and focusing segments to better cater to customers’ response helping maximize the impact of your marketing efforts.

For example, Optibot might notice that a specific demographic in a segment is responding better to a particular type of ad, or it might detect that a specific campaign is underperforming compared to others. It then provides AI-powered insights, allowing marketers to reallocate budget from certain campaigns to those yielding better results or adjust targeted segments to reach the right audience.

See depiction below:

Optibot saves brands valuable time and ensures every marketing dollar is spent efficiently. Instead of manually sifting through data and making educated guesses about what might work best, relying on Optibot’s data-driven recommendations to make informed decisions empowers brands to build long and healthy customer relationships.

The Guide to Re-Engaging Lapsed Customers

Identify at-risk and lapsed customers and bring them back with this guide.

In Summary

Optimove’s powerful AI ensures marketers are never too generous. But to extract the full value of AI, a strong data foundation and clean testing frameworks are essential. With everything in place, marketers will wrest control of generosity. It will be highly effective in keeping and retaining customers without being too generous.

Explore the full series by delving into each part:

For more insights on how Optimove’s AI can optimize generosity at scale, request a demo.

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Moshe Demri

Moshe Demri leads Optimove’s global revenue team and is focused on helping clients optimize their customer retention plans and their use of the Optimove software. Moshe has vast experience consulting clients as a data scientist, analyzing their customer data and revealing actionable, data-driven marketing insights. Moshe holds a BSc in Industrial Engineering and Management, specializing in Information Systems.