Most large and medium-sized companies generate a lot of data. Many know they should be using it, but don’t know in what ways or how. Data science can drive profitability and efficiency in a multitude of ways. It can help you choose the right price for your product or target the right people with marketing. It can turn a tedious repetitive Excel task into a seamless automated procedure. It can quantify how consumer demand will change in the south east when the weather is good and demonstrate this in a compelling, dynamic visualisation.
It’s impossible to know what solutions are possible for a company without having a look under the bonnet. We’ll carry out a comprehensive diagnostic review of your data, systems and in-house resources. This usually takes 1 to 2 weeks, depending on the company size, including writing up a report. The diagnostic report details will feedback the following:
- The current state of your data asset, in terms of format, accessibility and relevance.
- What ongoing tools, visualisations and metrics will improve efficiency.
- What analytics projects, if any, will add value to your company.
- What skill-sets your in-house team need.
There is absolutely no obligation to use us for implementation after the data review is complete.
Marketing Effectiveness Analysis
- It's no secret that much of what is spent on marketing is wasted.
- Research by Proxima finds as much as 60% of digital marketing spend is ineffective!
- Rigorous statistical modelling enables companies to understand how marketing levers are driving sales / profit / web traffic / conversion rates
- Models are used to calculate profit maximising investment budgets and optimised media laydowns.
- Good models expose synergy, halo and cannibalisation effects.
- DS Analytics are completely independent and ALL models are built for clients by highly experienced (at least 5+ years) econometricians and statisticians.
- We do not use black-box solutions, but rather custom build models using open source platforms R and python. All models are tailored to the business problem and designed to answer specific questions.
- We’re entering a new phase in marketing effectiveness modelling. The tools available are more powerful and the techniques more rich and varied.
- It is essential to choose a solution that answers the business question, rather than one that allows the analyst to show off a new skill or piece of software.
- All analyses begin with a detailed consultation. All models incorporate business knowledge as well as data. The answer is never just ‘in the data’.
Pricing and Promotions Analysis
- Finding the optimal SKU price is essential for all retailers.
- In-depth statistical analysis can uncover the relationship between price and demand, helping decision makers choose price points that maximise profits / market share / customer satisfaction.
- Promotions analysis enables companies to target customer segments with the right promotional activity at the right time.
- DS Analytics build scenario planning tools that enable our clients to investigate the impact of different strategies on KPIs. These are dynamic, interactive web apps, with carefully designed, tailored user experience.
- This analysis will help answer questions like:
- What is the likely impact of a 5% increase in price on web traffic, web sales, awareness and market share?
- Are your tactical pricing strategies achieving their goals?
- What promotional strategy will shift product X without cannibalising core product sales?
- Predictive modelling enables companies to anticipate customer behaviour and act fast.
- It can answer questions like:
- Which customers are we most at risk of losing?
- How is demand likely to change with forecast economic changes?
- Has a new feature on our website increased conversion rates?
- When is a utility likely to fail?
- How much inventory will we need over the May bank holiday weekend?
- Customer product recommender systems pair customers to products based on behavioural and attribute data.
- Market leaders like Amazon and Netflix have been using these for years to drive up sales in highly competitive markets.
- DS Analytics help retailers build and integrate such systems, now an essential part of any e-commerce platform.
- Customer segmentation involves dividing up your market into discrete groups with distinct characteristics and behavioural patterns.
- Segmentation analysis can improve marketing effectiveness, customer targeting and relationship management.
- It can help with product development and help you to identify gaps in the market.
- Segmentations use customer data, either behavioural (e.g. behaviour on your website, purchase history, purchase frequency) or demographic (e.g. income group, occupation, gender etc.).
- The outputs are
- Segment groups, with behavioural and characteristic descriptions (e.g. high price sensitivity, high purchase frequency, aged 25-35, travels 5 miles to store etc.)
- Group size and significance to business
- Tagged customer IDs
- Outputs are presented in interactive apps and widgets, with careful UX design.
Business Simulation Modelling
- We’re considering holding a large conference to market our service. What are the best, likely and worst-case profit projections over the medium to long term?
- What headcount are we likely to need in 5 years? At what rate should we be recruiting?
- With what likelihood will we achieve our 5 year customer acquisition target? What minimum marketing investment is required to have a better than 80% chance of achieving that goal?
- Business systems models enable decision makers to understand the likely impact of different strategies.
- They allow you to understand the trade-offs involved between alternatives.