How to know when your business is ready to build Customer First data science capabilities: a dunnhumby perspective by David Ciancio
As a retail organisation transforms itself along the path to becoming Customer First, its own internal capabilities should organically mature in step with this transformation; an exemplary maturity curve is illustrated below.
As the organisation learns and grows around the Customer, it may wish to onboard some enhanced Customer-centric services or technologies formerly provided by outside partners to an in-house team. We strongly believe, in fact, that accelerating internal capabilities should be an ambition for all services or technology partners of the business; we are ourselves deeply committed to this goal. In-housing can take the forms of transferred skills and knowledge from within a co-creation model, or as the outcome of a strategic Build-Operate-Transfer (BOT) plan to deliver specific activation tools or platforms. We illustrate several service-delivery options and levels of internalisation in a chart below.
Of course, Customer First must always be at the centre of the consideration of whether to bring certain capabilities in-house – and doing so must deliver better decisions for Customers. For examples, by taking a certain tech solution or service in-house, Customers should receive better service, save money when costs are reduced, benefit from increased efficiency and accuracy, or feel more secure in providing their personal information.
To help assess progress along the Customer First journey and readiness to in-house more advanced elements of Customer data science, analytics, and technical tools or services, we recommend the following checklist, which is derived from our earlier perspectives on the 5 Steps to Becoming a Customer First Business as well as from best practices amongst our global clients who are farther along their journeys.
Ten key steps to implement before in-housing Customer-centric technologies and capabilities
What should be true as foundational business as usual practices as you transfer capabilities to internal teams along your Customer First journey. You can confidently state that your organisation continually makes decisions wherein you:
Example: dunnhumby Build-Operate-Transfer (BOT) Delivery Options
Project-Based | Solution Provider | Managed Service | Integrated Service | Advanced Data Science Service | Self-sufficiency / Turnkey Service | |
overview | Short-tern discrete projects involving consulting and analytics | Specific solution or suite of solutions from provider portfolio | Dedicated team and access to provider products and services | Dedicated team with access and integration of specific products and services | Streamlined advanced data science team | Software as a service Science as a service |
People | Person-day specific access to consultants or applied data scientists in local market | Fixed allocation of time from consultants, applied data scientists, data engineers in local market | Full-time access to named consultants, applied data scientists, data engineers in local market and support of regional / global experts | Like Managed Service with support for integration into 3rd parties and passing some process to internal team to operate | Dedicated data science team Consultants on key projects and initiatives | SLA support only. Potential transfer of people in provider operation roles to internal roles |
Process | Project deliverables, outputs, presentations | Solution deployment and delivery, with recurring services as required | End-to-end support from strategy to execution Change management support around internal capabilities and processes | Integrate Science into specific 3rd party solutions Ingest outputs such as flat files, data feeds, data flags back to internal systems License and train internal teams on specific methodologies and approach | Integrate Science into internal systems | Direct access to self-serve products and science APIs Transfer day-to-day management activities to internal team |
Application | N/A | Software products on private cloud | Software products on provider private cloud – with ongoing upgrades | 3rd party integrations Software products on hybrid cloud with ongoing upgrades | Data science APIs into internal built products and workflows | Software products on hybrid cloud Machine learning/data science via APIs |
Data | Data Lake | Data feed from internal to provider Data Solution | Data feed from internal to provider Data Solution | Data feed from internal to provider Data Solution and data feedback to internal systems | Data feed from internal to provider Data Solution and data integrated feed into internal Data Solution | Internal data solution – no data feed to provider |
Keeping Customers in the Decision Process
In a companion paper, Five Customer First Rules for Retailers: the new tech edition, we concluded that a decision process on whether to build or buy a software solution must also consider the impacts on Customers resulting from tech and service decisions, and be mindful of effects on Customer experiences.
Similarly, Customers should also be at the centre of the consideration of whether to bring certain capabilities in-house. Applying this checklist will help you to know if you are thereby delivering better decisions for Customers – the key question to help you make the right choice along your journey.
For additional support on completing the checklist or determining the right Build-Operate-Transfer delivery model for your organisation, please contact your dunnhumby representative.
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