Data Analytics Consultancy London
Research from TDWI revealed companies using analytics for decision making are 6% more profitable. Data and analytics are abundant resources, business requires new solutions to be predictive and proactive, to help retain and satisfy customers and to increase operational efficiency. However, it is not as simple as inputting your data into a business intelligence tool and expect groundbreaking results.
Whether your aim is to improve your marketing by mining customer data, securing your infrastructure or creating predictive models by gathering data from disparate sources – following best practices ensures you’re making intelligent, data-driven business decisions. Analytics teams must align senior business leaders to the vision and goals of any data analytics initiative, be it to help promote growth or operational efficiency. Applying data hygiene, matching the right data, methodology, collaboration and governance model.
1. Audit Internal Maturity
Before investing in magic-bullet solutions, companies should audit the maturity of internal capabilities; it’s important not to ignore the work and simply hope success will magically happen.
- Ability to measure performance metrics per set goals
- Establishment of measurable goals/KPIs
- Success in securing budget/resources
- Ability to create actionable predictive models
- Quality and completeness of customer data
- Organisational buy-in
2. Develop an ROI Culture
As the use of customer analytics gain importance and companies leverage legacy information and Analytics infrastructures. It’s important to realise that success with advanced analytics requires both technical know-how and a thoughtful approach, without sound project controls, costs can spiral. We recommend developing a ground-up return on investment-driven culture.
3. The Right Data
Any Data analysis is only as good as the quality of the data going in, so proper data hygiene is essential.
- Assemble comprehensive data documentation to map and understand the process flows
- Document assumptions and techniques used to build a master data set
- Ensure that the business has the right variables available
- Data must align with key business performance metrics, and should allow executives to answer pressing business questions
- Keep data current, by measuring and refreshing at appropriate times
4. Data Depth
If you’re executing a customer analytics program, collect everything. The attributes of a customer are critical for a range of analytic purposes including:
- Demographic data
- Products purchased
- Financial records (invoices, payments, etc.)
- All customer interactions – for example, calls to the contact center, visits to your website and to retail stores
- Letters and surveys
- Emails, text messages, social media posts, and service calls
5. Measure the Right Things
You can’t optimise what you don’t measure. There is not a one-size-fits-all solution. Eliminating extraneous data will simplify visualizations and allow executives to focus on relevant metrics to make decisions. In the example of an e-commerce solution:
- Which channels drive the most conversions?
- What are your leaking buckets (places where people leave the website)?
- Whether people use multiple devices before purchasing products?
- What are the look-to-buy ratios for your individual products and product categories?
- What landing pages need to be improved and in which channel?