The Customer Intelligence Checklist

Following on from my posts a couple of months ago, I’ve outlined a few tips that you may find useful to help you to develop customer intelligence within your organisation:

1. Ensure that the customer intelligence objectives are in line the organisations strategic objectives. 

Customer intelligence objectives and strategic objectives shouldn’t be treated as two separate entities by the organisations. The customer intelligence objectives should form part of the strategic objectives. If they remain separate, they run a very high risk of being treated as abstract targets. It also means that they treated as an afterthought, with the findings likely to be overlooked and less resources available to develop customer intelligence. Get top level buy-in!

2. Demonstrate the benefits of the customer intelligence to the organisation.

So much energy can be expended on collating customer data and creating intelligence, that its successes as a result are not communicated effectively enough. Developing customer intelligence is a continuous process and the collection of customer data is usually on top of a persons ‘day job’. The inevitable ‘what on earth are we doing this information’ question will arise, so its vital that the improvements being done as a result of customer intelligence is regularly marketed to maintain buy-in. 

3. Justify the need for customer intelligence.

Cataloguing and measuring the successes as a result of customer intelligence is important to demonstrate its cost benefits to the organisation. Developing customer intelligence can be an expensive process for organisations, so you should be measuring the impact to justify the need for it. 

4. Plan ahead

The first step of customer intelligence is to be clear on the objectives behind developing it. By its nature, customer intelligence is meant to be a proactive process with an aim to achieve something for the organisation. You need to be aware of what the organisation wants to simply reacting to things that have already happened and risk the intelligence becoming out of date and irrelevant.

5. Use your customer intelligence.

Customer intelligence is about tailoring products and services for customers. It’s about improving the customer experience, so the organisation gives itself a better opportunity to identify and meet their needs and preferences. The organisation’s customer intelligence process needs to be built into its customer strategy to ensure that its customer groups are identifiable and that their needs and preferences are regularly identified and affirmed.

6. Customer intelligence doesn’t stand alone
Customer intelligence related work should not be treated in isolation. Compare it to previous information and outcomes. Use it alongside benchmarking information and market intelligence to develop a better a better competitive advantage for the organisation (Grimes, 2009).

Ok well that’s it for today..... Next time: a crowdsourcing software review!

References

GRIMES, N., 2009. The nine steps to best practice customer insight, My Customer.Com. [online] Available at: <http://www.mycustomer.com/topic/customer-intelligence/best-practice-insight-what-essential-invisible-eye> [cited 30 October 2011].

Mining our knowledge Part 1

“The future success of companies and organizations will increasingly be based on their ability to unlock hidden intelligence and value from unstructured data, and text in particular”

(The 451 Group, 2005)

The Information Age has made organisations data rich, instead of being starved of information; they now have volumes of data available to them. This presents challenges because organisations often lack the resources to convert them into useful intelligence.

Amongst this data is qualitative data i.e. a person’s opinion of a product or a service, which can be far more useful in developing insight instead of hard facts and figures. However translating qualitative data into knowledge is really hard work. This is because this information is usually collated in a text form made up of observations and quotes, and it takes a lot of organisational resources including time, people and technology to develop it into useful intelligence.

One method available to organisations to develop this knowledge is text mining. Text mining has traditionally been associated with the academic and research fields, but now its becoming more widespread with commercial organisations recognising that the unstructured data that they hold is as valuable as their structured data since it offers a more holistic view of the organisation. As a result many companies are offering text analytics, the text mining equivalent used in business settings as a solution for analysing this data (Feldman, 2004).

So what is text mining?

Text mining is “the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources… linking together the extracted information together to form new facts or new hypotheses…” (Hearst, 2003).

The key tasks of text mining include:

·         Classification - The process of tagging new data based on its features.

·         Clustering - The process of grouping documents based on the similarities in the content

·         Association - The process of organising information into hierarchical networks. (Leong et al., 2004)

Transforming unstructured text into intelligence

Qualitative data can be taken from many different sources including emails from customers, employees and suppliers, research reports. It can also be published information about competitors such as promotional materials & customer comments, and information related to legislative and regulatory changes (Leong et al, 2004).

This data is turned into a structured and a relevant form. The “mining process” is made up of three elements:

1. “information selection and preprocessing;

2. patterns analysis, recognition and visualization; and

3. validation and interpretation” (Zhang and Segall, 2010, p.625).

Mining this information enables organisation to enhance their business intelligence (BI). Wang and Wang (2008, p.623) state that “the central theme of BI is to fully utilize massive data to help organizations gain competitive advantages”. BI helps organisations to discover the knowledge hidden in their data.

Text mining (TM) helps organisations to develop knowledge using “various algorithms and tools to extract metadata or high-level information and/or to discover patterns and relationship within the extracted information” (Choudhary et al. 2009, p.730). The subsequent knowledge can help decision makers to make more informed decisions.

The difference between data, text and web mining

Zhang and Segall (2010 p.625) highlight the differences between data, text and web mining stating that “Data mining primarily deals with structured data organized in a database. Text mining most handles unstructured data/text. Web mining lies in between and copes with semi-structured data and/or unstructured data”.

Ok so that's part 1, in part 2 I’ll outline  the ways in which commercial organisations are using text mining to move their business forward. See you next time!

Note: I developed this post a while ago, initially as part of an essay for the MMU Information and Communications department.

References

CHOUDHARY, A.K., OLUIKPE, P.I., HARDING, J.A. and CARILLO, P.M., 2009. The needs and benefits of text mining applications on Post-Project Reviews. Computers in Industry[online]. 60 [cited 12 March 2011] pp.728-740.

FELDMAN, R., 2004. Text Analytics: Theory and Practice, ClearForest Corporation. [online] Available at: [cited 27 March 2011].

HEARST, M., 2003. What is text mining?. Berkeley, [online] Available at: [cited 2 March 2011].

LEONG, E.K.F., EWING, M.T. and PITT, L.F., 2004. Analysing competitors’ online persuasive themes with text mining. Marketing Intelligence & Planning [online]. 22 (2) [cited 14 March 2011] pp. 187-200.

THE 451 GROUP, 2005. Text-aware Applications: The Endgame for Unstructured Data Analysis. cited by CLARABRIDGE, 2008. Text Mining’s Moment: The three trends triggering commercial adaptation. clarabridge.com, [online] Available at: [cited 1 March 2011].

WANG, H. and WANG, S., 2008. A knowledge management approach to data mining process for business intelligence. Industrial Management & Data Systems [online]. 108 (5) [cited 12 March 2011] pp. 622-634.

ZHANG, Q. and SEGALL, R.S., 2010. Review of data, text and web mining software. Kybernetes [online]. 39 (4) [cited 1 March 2011] pp.625-655.

Customer Intelligence:The game changer - Part 1

The phrase 'know your customer’ is an often repeated mantra, in today’s business world. It’s a phrase that’s uttered in high level meetings and in weekly performance appraisals. The point is, that it’s vital for organisations to ‘know who their customers are’ and ‘what they need or want’, to reasonably tailor their services to meet those requirements. 

As customers ourselves, we ‘enjoy’ elements of personalisation in the services that we receive, whether that’s through Tesco’s club card scheme, personalised recommendations based on previous purchases from Amazon or being able to like companies on Facebook which push news to us in our activity streams. We are getting used to this type of service and the core enabler behind this is customer intelligence. It’s also known as customer insight and customer knowledge.

We are living in the Information Age according to the academics. We've gotten data rich because of the web, we even contribute to it by generating content  even to the point that we have to mine our data to make sense of it (a future post). We now have the tools to learn more about our customers but so do our peers or competitors. If we fail to understand to what our customers need or want, they will go to somebody that does. (check out my post on how all of us are making the web better)

This applies to the social housing sector as well. On the face of it, demand for social housing exceeds supply, which limits customer choice but tenants aren't the only customers. Stakeholders like the Homes and Communities Agency and the Banks are also our customers, we need to demonstrate that we are delivering good tailored services to our tenants to engage these stakeholders trust and confidence.  What’s at stake? Well a simplistic answer is organisational growth.  Reduced backing from these bodies would result in insufficient resources for developments and stock transfers. (Check out my posts on what social housing is and a brief history of social housing to find out more about the sector).

What is customer intelligence?

Customer intelligence is the process of using relevant information to develop a better understanding of customer preferences, beliefs and aspirations to enable the design and delivery of better and more personalised products and services.

Customer intelligence can help organisations to:

• Streamline its processes for delivering their products and services based on a better understanding of customer beliefs and expectations.
• Identify their priorities to enable the easier allocation of resources
• Develop better tailored products and services based on the customers requirements.
• Improve customer service levels and increase satisfaction rates

Above all else customer intelligence should be used by organisations to inform their strategic planning and should be used as tool in the decision making process.

Two pitfalls lot of organisations fall into, include treating the customer intelligence process as just data collation exercise, forgetting that data needs to be developed into useable knowledge for the organisation. Customer intelligence isn’t about collecting data, it’s about what you intend to do with it. The other thing organisations need to watch out for is treating the customer intelligence as a one off process; it’s a continuous activity which helps organisations to grow its knowledge of its customers. Remember, customer needs and preferences change, customers change for that matter (IDEA, 2009).

Creating Intelligence

The process of developing customer intelligence, in my opinion is linked to knowledge development within organisations. Customer intelligence begins life as unstructured raw data gathered through consultations, surveys and a variety of other methods. The data is contextualised and organised in relation to the organisations targets or key performance indicators to organise it into facts and figures to convert it make it useable information. Knowledge is created when the information and is considered & analysed in relation to the organisations ethos and goals. Previous experiences are applied to the information by the organisation to reflect and improve itself and this is the stage where simple information becomes organisational knowledge.

The following diagram outlines this process:

The process of converting data into information and transforming information into organisational knowledge

This is the end of part 1, in the next instalment, I will propose a few questions that organisations should ask themselves when they are developing their customer intelligence. I will also outline the key elements to consider when developing customer intelligence.  See you next time!

References

IDEA, 2009. What is customer or citizen insight? [online] Available at: <http://www.bbc.co.uk/news/uk-14380936> [cited 30 October 2011].