How engaging with us works
Our mission is to make data intelligence simpler for everyone, so we make sure to guide our clients through their business data challenges and tailor the path to successful project delivery based on each unique case. See how we help clients through the analytics process and how we deliver value.
What does a customer analytics project typically look like?
Initial meeting and discovery phase
After the initial introductory call to see if we are a mutually good fit, we run a discovery phase with the client. The goal is to diagnose the challenge, and to analyze the as-is state, what the potential solution might be, and the target state. We often run workshops with the main stakeholders from your company to agree on the value to be delivered, what roadblocks need to be overcome, and agree on potential solutions.
Brainstorming and initial data assessment
We make sure everybody in the project team understands the project assignment, and we go through brainstorming and discussions on the solution approach. An initial client data assessment typically includes evaluating the current state of the customer data landscape in terms of reporting, data quality, or availability of data necessary for subsequent activities (e.g., customer churn). Next, we assess which customer analytics can be done or not (e.g., poor data quality, insufficient data). We define a set of improvement opportunities for the current customer data solution and effort estimation. The target data landscape with a roadmap describing the journey to reach the target is emerging.
Our delivery team puts together a solution proposal that includes the scope and agreed approach, what are the deliverables, the cost of our work, what is considered to be a successful delivery, and what are potential limitations, and how we deal with them. This proposal is subject to the client’s review and sign-off and can be amended during the sign-off process based on changed requirements. The price of the project will be influenced by the amount, quality, and simplicity of your data, as well as the level of analytical complexity you are looking for (e.g., whether machine learning or AI will be used in the solution). We will be happy to tailor the solution to your needs.
Technologies we use
We have demonstrated experience in tool development including methodologies. Due to our experience, we can build upon an existing and thoroughly tested framework. We have a fully available expert analyst and data science team designated for the project. We have proven experience in data integration from various BI/DWH projects around the world. To deliver value in our projects, we have been using technologies such as Oracle, Microsoft, Teradata, Tableau as well as open-source projects like Hadoop and Spark.
Proof of concept (POC)
We prepare initial analytic insights and a sample proof of concept model for the client. This includes data modeling and integration or data profiling as initial steps before developing data science models. We make sure to stick to an iterative cycle where our expert team presents initial results to a client to make sure we are on the right track, get immediate feedback for any tweaks needed, and keep the client involved. Such an approach enables us to deliver value to our clients early in the process. During this phase we shape the final model.
Implementation and recommendation
Once the data model is final, we make sure to deliver the results and recommendations in an understandable and visually engaging way to our client. Typically, the deliverables include BI reports, spreadsheets, calculations, and key takeaways in a document or slide-deck. We agree on the output presentation, next steps towards implementation, and on the output transfer.
Rollout and evaluation
Once the recommendations are implemented by the client, the project doesn’t end for us. The important part of our delivery is measurable results. We make sure to evaluate the solution based on the metrics agreed. It may happen that the results, the initial assessment, or the analytics process uncover the need to address other challenges to get even better results (e.g., we successfully manage to increase customer loyalty, but one of the recommendations would be to increase the data quality for even better results).