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Software as a Service

SaaS is evolving and as the number of competitors on the markets is skyrocketing, AI is becoming a decisive differentiator for success. The key challenges that SaaS companies facing is often twofold. On the one side they need to investigate where in their business model those 10x improvement levers through AI are. On the other side their engineering teams need to develop new set of skills to turn vision into action.

As data collection and AI grows in demand, the availability of AI- & ML-services has commoditized and we are seeing a similar development than for cloud computing in the last decade. This implies that the success factor in applying AI for SaaS companies is not to built superior models, but rather to train available data in the best possible way so the models can suite the needs of the business context. This is a major shift in the role model of engineers from writing algorithms to training of applications and come with challenges of its own. Key success factor on this journey is to have training data at sufficient quantity and quality as well as further data sets to test the model prior to moving into production.

AI- & ML- algorithms are in general designed to serve on of the following purposes:

  • Diagnose a situation / pattern recognition
  • Predict a future outcome
  • Prescribe actions in order to optimize along certain dimensions
  • Personalized user experience
Based on their business model and customer value proposition, companies need to identify areas where algorithms have the potential to increase status quo by 10x and also determine what data is needed for this.

  • "Finding right prices for products we sell-off on the secondary market was a boost for our valuation without changing any processes."

    CEO of Circular Economy SaaS Start-Up

  • "We are fastly growing and collect a lot of data in the sales process as well as through our platform without utilizing this data. Fitting this data into models that work for us has been a game changer."

    CTO of European SaaS Provider (B2B)

  • "Finding right prices for products we sell-off on the secondary market was a boost for our valuation without changing any processes."

    CEO of Circular Economy SaaS Start-Up

  • "We are fastly growing and collect a lot of data in the sales process as well as through our platform without utilizing this data. Fitting this data into models that work for us has been a game changer."

    CTO of European SaaS Provider (B2B)

    Pre-configured Analytics & Machine Learning systems​

    Complexity of adressing business-specifics is often underestimated while technological requirements are overestimated
    Lack of understanding in business units on how to utilize Advanced Analytics and Machine Learning
    Access to experts with the right set of competencies across different domains
    Availability of data at sufficient quality and quantity

    Performance Drivers

    Performance DriversLead Scoring & Qualification
    SaaS companies can gain advanced insights on their leads and different customer groups through analyzing data at later stages of the customer lifecycle. Through lead scoring models sales people will know on which leads to invest time and how to package personalized offers.
    PricingMargin increase through optimized pricing
    SaaS companies can optimize their pricing strategy by identifying key value items, i.e. products for which price is exceptionally important. In addition, companies can use competitive intelligence through web crawling to adjust their prices dynamically.
    Customer churnRevenue increased through improved Customer relationships
    Companies can identify likely churners and identify offers that will convince them to stay.
    Recommendation engineIncreased revenue through higher sales volume
    Companies can predict how customers respond to service offerings & product campaigns based on assessment of historic behavioural patterns.

    Next Steps

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