This is the second entry in my series interpreting what it means for Chief Marketing Officers to become analytical marketers. Part 1 in the series, which covered planning, can be found here. This post describes phase two, the project kick-off. I'll start by listing phase two objects, and then provide details on how to achieve them.

OBJECTIVES OF PHASE TWO

• Develop a small, core team to lead the later phases of the project and a wider team who will take on specific tasks and shape details.
• Get a pilot project underway that will deliver a quick win to quieten the sceptics.
• Understand what customer data and information you already have at your disposal.
• Deliver departmental data stores and analytics.
• Create the first (analytics- and deliverable-focused) departmental metrics and incentives.
• Prototype a working methodology (and tools) for sharing knowledge.

PROJECT PHASE TWO, STEP ONE – THE KICK-OFF MEETING

Vital to the success of any change program is the explicit support of the senior executive team (in most organisations, those with the most influence to change our behaviour are our immediate line managers and the Chief Executive Officer). Plan a high profile kick-off meeting with senior executives and the active participation of the CEO. Task the management team with cascading down the project rationale and cascading up constructive feedback (and, hopefully, names of volunteers). The CEO needs to personally commit to the project, being a vocal advocate throughout its entire lifetime, including the inevitable times when there are setbacks or progress is slow.

PROJECT PHASE TWO, STEP TWO - THE FIRST 90 DAYS

After putting the core leadership team in place, use them to lead the search for colleagues who have experience with analytics to build a wider implementation team. Whilst this team does not have to be formed only from people with in-depth or extensive hands-on experience, the members must be able to help shape a more detailed short-to-medium term plan. Neither do they need to be full time secondees. Perhaps moving in and out of the project team as required is a good balance (however, there must be a commitment to keeping the project sufficiently staffed to keep it moving forward).

Simultaneously (and probably a good place to find staff with some analytical experience), identify a program within a customer-facing department in the organisation that has already delivered a quantifiable business benefit from the use of analytics in some form. Ideally, this program will have potential for expansion, especially if there is an immediately recognizable short-term win. Use this program to establish and benchmark the benefits that are being delivered now (including any specific improvements since the program was started). Documenting the existing value of analytics helps to offset the influence of those members of staff who may be sceptical (or even cynical) about the value of the project. If a suitable program does not exist, then look for a change program that exhibits a willingness to introduce innovation based on improving performance against a quantified marketing metric.

Embark on a quick-win, department-level analytical marketing project. It is more important that the project delivers a real, measurable, relevant benefit than that it be completed within a specific time line (all to often 'business-as-usual' improvements are reclassified as quick wins to give the impression that progress is being made). Business Analytics has a specific advantage in that it can show potential from improvements almost immediately (through doing quantified analyses), even before a fully operational system is complete. For example, forecast uplift in sales based on a more rigorous analysis of the customer base to tune segmentation for the next campaign. This pilot project should include specific measures of performance, with incentives to drive adoption as well as be provisioned with the appropriate tools.

The core leadership team, working with the wider project team, should then begin detailing and quantifying some of the specific objectives and milestones for the overall project, paying particular attention to specifically measurable business benefits. This is turn should lead to a more detailed version of the overall project plan. At this stage it still does not need to be perfect but it does need to be as specific as possible whilst remaining believable.

Also begin an enterprise-wide customer data audit to identify what data and information is currently being gathered and stored about customers. Where is it, how is it stored, used and is it accessible for other software tools? From this audit begin to build a catalogue of data fields that could be build into a framework to support other analytical projects. If a data mart or data warehouse exists, think about how it could be augmented. If one does not exist, start to build a small, departmental-level data store that holds the best quality data you can make available to it.

Running in parallel with the customer data audit should be a project to document existing data capture, processing and governance processes. Right from the outset, the project team needs to be thinking about formalising the Enterprise's knowledge sharing regarding what data and information assets it has, how they are managed and made accountable and used as the basis for exploration of future projects. Whether you use a formal knowledge management or knowledge sharing tool is up to each organisation, but the principle should be firmly established (even if the tools need later revision).

Check back soon for part three of this series where I will discuss:

• Developing a formal customer analytics strategy.
• Writing role descriptions for analysts.
• Aligning the goals of marketing to those of the wider enterprise
• Establishing the foundations for an analytics centre of excellence.