Business Opportunity Assessment Results

Some clients wanted a fast start to deliver customer insight. They understood that valuable information lay dormant within their systems. It was just a matter of unlocking the potential. The Business Opportunity Assessment delivers that insight.

This is an example project report. It provides typical results generated by the Business Opportunity Assessment.

The results are for Big Office Suppliers (BOS):

• Established in 1998, BOS an established supplier of office equipment, supplies and stationery to small and medium sized enterprises
• BOS receives orders directly to their Reading call-centre in Berkshire, and on-line via their website
• All scenarios and examples are based on real-life examples from our client base

NB. BOS is a fictitious company used solely for demonstration purposes.

Data analysis objectives

The objective was to demonstrate the combined value of customer, prospect and transactional data with the aim of investigative analysis in readiness for new marketing initiatives to:

• Understand customer purchasing behaviour
• Manage customer retention
• Cross-sell and up-sell products and services
• Transform low performance customers into top performers

Assimilation of business processes & systems

It may seem obvious, but before getting embroiled in data, it is essential to consider how a business operates. Discussions were held with the BOS management team; sales, marketing and IT to address these questions:

• What does the organisation do?
• What are the benefits to their customers?
• How do they actually make money?
• What systems support their business operations?

The answers provided understanding of the business, its processes, supporting systems and data.

BOS have four systems:

• The Sales Order Processing system is used directly by the customer services team to place customer orders. This system is also used to manage price lists and customer discounting.
• The website manages on-line ordering, it is directly integrated with the Sales Order Processing system. So other than a slight time lag, customer details and their orders placed via the website are available via the Sales system.
• The Customer Relationship Management system is not currently integrated with the Sales Order Processing system. The system has been loaded with customer names and addresses. When new customers make an enquiry or request a brochure or catalogue, they are entered into CRM.
• Marketing communication is a function rather than a system. BOS sends e-newsletters to on-line customers and people who have registered for the BOS newsletter. Brochures and catalogues are mailed to all customers. Customer communications history is not specifically maintained, but the contact records for customers sent the BOS newsletter are available in spreadsheets.

The outcome was a detailed requirement for data from the various in-house systems:

• Customer and prospect organisation names and addresses
• Contact names
• Product or service details
• Customer service enquiries and actions
• Quotations, orders, sales and invoices
• Sales reps, departments, branches, regions
• Communications history

Data load and appraisal

Upon receipt from the client, all data was checked and loaded into a single database in preparation for review and analysis.

The data underwent an appraisal to highlight issues concerning data quality and integrity that would affect the outcome of the analysis. This included:

• Analysing data values, frequency, patterns and format
• Discovering important data statistics (Min, Max, Nulls and distribution of values)
• Identifying redundant or duplicate data
• Reviewing data integrity

Some issues were identified within the systems data; inconsistent data values and duplicate records. These issues were addressed to provide a robust basis for analysis.

To facilitate data analysis derived fields were created and populated:

• Summarised and average values: sales and margins
• Response and time elements: date of first and last orders, duration of custom
• Customer scores: Recency, Frequency and Value (average gross margin)
• Market basket combinations – i.e. which product combinations were purchased

Exploring customer data

Armed with hypotheses and areas to explore, but before commencing, it’s best to understand how many customers there are and which parts of the organisation they touched:

Findings:

• The term customer refers to both those who have made a purchase and prospective customers e.g. those who have registered for e-newsletters and or made an enquiry
• Not all web orders have been transferred to the sales system; they are only transferred once the order is completed by the customer
• The CRM has bee updated with the latest customer sales orders
• Marketing is lagging behind, sending out lots of e-newsletters, but many of these are prospects, hence the larger total customer figure

With these figures and each project objective in mind, each train-of-thought analysis is documented:

• Objective of the analysis
• Logic applied
• Findings from the analysis: tables, graphics, etc
• Applied business observation

What is the geographic distribution of customers?

ANALYSIS

Reviewing the customer base as a whole i.e. including prospects, where are the greatest concentrations of customers?

LOGIC

Identify the primary address for each business within a postal district i.e. multiple addresses for the same organisation essentially at the same location will be counted only once, where as more widely distributed offices will be counted separately.

FINDINGS

APPLIED BUSINESS OBSERVATION

Whilst customer distribution is widely spread, the highest concentrations are within industrial areas.

• Are companies in these areas specifically targeted or are they simply the result of customer demand?
• London and inside the M25 does not appear to have such a strong concentration of customers. Why?

What does the year-on-year order profile look like?

ANALYSIS

Compare the list of customer’s who purchased in each of the last three years.

LOGIC

Summarise sales for each year: 2006, 2007 and 2008 against each customer (noting figures for 2008 are year to date).

FINDINGS

Customers placing at least one order:

APPLIED BUSINESS OBSERVATION

36% of the 4,879 customers placing an order in 2006 haven’t made any further purchases.

Similarly 34% of the 5,949 customers purchasing in 2007 haven’t made further purchases.

It would appear that there is considerable acquisition each year, but a considerable proportion of these customer’s are not retained in subsequent years.

Taking the 2007 average spend of £1,347, if there was a 5% improvement in customer retention between 2006 and 2007, sales would have increased by c. £350,000 in 2007.

How well do we retain our customers?

ANALYSIS

How long do businesses remain active customers i.e. they purchase office supplies?

LOGIC

Identify the date of customer acquisition and the date of last purchase:
Noting that each customer may have more than one account, group customer account records, establish the earliest date an account was opened and the most recent purchase date.

FINDINGS

APPLIED BUSINESS OBSERVATION

With the notable exception of 2004, 30% of customers are lost within the year of acquisition. There is then a steady decline year-on-year in customer retention. Overall there appears to be a year-on-year decrease in customer retention.

• What acquisition strategies are used to attract new customers?
• Are customers incentivised with limited special offers?
• How quickly are these offers removed from newly acquired customers?
• What would be the value of achieving a higher retention rate?

What is the customer lifetime value of our clients?

ANALYSIS

What is the value of a contingent of customers over their lifetime? Use a classic CLV model to determine their value and evaluate the opportunity derived from increasing their lifetime value.

LOGIC

The model calculates the value of each customer acquired in a particular year. The contingent acquired in 2005 was taken, providing a full three years of subsequent purchases.

The total value of their transactions in the year of acquisition and each of the two subsequent years was calculated along with the mean value.

By reviewing the data some very high value outliers were identified and removed to obviate skew and the data set restricted to the revised mean +/- 2 standard deviations in each case.

The annual values were discounted so as to be comparative year-on-year using the standard discounted cash flow formula, applying a general rate of 8%.

FINDINGS

Customer Lifetime Value Model (1):

Re-worked Customer Lifetime Value Model (2):

APPLIED BUSINESS OBSERVATION

The lifetime contribution per customer (CLV) in Model (1) is showing healthy growth, all be it for declining numbers of customers.

The model allows a “what-if” scenario to be evaluated. The second table shows a re-working of the model using the same contingent but with a modest 5% improvement in retention each year.

The cumulative net present value of the contingent of members (CNPV) increases to £5 million – an increase in revenue of over £260,000 for those customers acquired in 2005.

NB. This refers to only one annual contingent. A similar situation exists for every year’s acquisition of new customers, thus compounding the value of the opportunity.

What is the potential for cross-selling and up-selling?

ANALYSIS

How much potential business are we missing out on or loosing to our competitors?

LOGIC

Identify which customers have purchased office equipment and or office supplies and stationery. Total the value of their purchases, count the number of customers and band these by spend.

FINDINGS

This analysis compares the number of customers taking office equipment vs. office supplies and stationery:

• Purple shading = cross-sell potential i.e. purchased equipment but not supplies or stationery or supplies and stationery but not equipment
• Green shading = up-sell opportunities i.e. spend in one area is disproportionate to the other

APPLIED BUSINESS OBSERVATION

Taking the average customer spend of £1,347 per year, and assuming a conversion rate of 15% of the cross-sell potential (2859 customers) this equates to: c. £575,000.

Similarly, taking the average customer spend of £1,347 per year, and assuming a conversion rate of 15% of the cross-sell potential (2859 customers) this equates to: c. £250,000.

What combination of products and services are purchased?

ANALYSIS

Undertake a market basket analysis; identifying the combinations of products and services each customer purchases, ranking these by relevance and potential for cross-sale to other customers.

LOGIC

Taking products from the 2008 catalogue, identify each customer that made a purchase of each of these products in 2008.

Group the results, counting the number of customers that purchased each combination of products.

Identify those customers who purchased only one of any given combination and rank the results by sales potential.

FINDINGS

APPLIED BUSINESS OBSERVATION

There is good potential for cross-selling; the above provides an example of the many products in the catalogue.

It is recommended that the market basket analysis model be implemented with the results used to drive CRM, support Tele-sales and product promotions on the companies’ website.

How often do customers purchase our products and services?

ANALYSIS

Who are the highest and lowest performing customers? Use a classic RFV (recency, frequency and value) model to determine their value.

LOGIC

How long has it been since the last order?

• Most recent (<=30 days)
• Medium recency (31-60 days)
• Low recency (61+)

How often does the customer place an order?

• Most frequent (at least once every 30 days)
• Medium frequency (31-60 days)
• Low frequency (61+)
• Single purchase

What is average margin per order?

• High value > £100
• Medium value £26 - £100
• Low Below £25

FINDINGS

APPLIED BUSINESS OBSERVATION

Combining the best customers with the good customers, those with at least medium recency, frequency and value, there are 2,150 in this contingent. These customer accounts need to be carefully managed to ensure they maintain their value to the business.

There are 1,400 recent, low value customers that purchase frequently. Taking into consideration order and invoice processing costs, these low worth (average order margin) customers are considered unprofitable business. These customers need to be targeted with propositions to encourage larger, perhaps less frequent orders to increase their profitability.

Whilst not in the best customer contingent as yet, there are 668 high potential customers. These customers have made their first purchase recently and show promise for being long-term high value customers. These customers should be managed / targeted to encourage further purchases.

he Business Opportunity Assessment releases the inherent value locked inside your data. It delivers customer insight and business intelligence. The results provide immediately actionable opportunities to improve your sales and marketing.

Getting the information you need

In today's turbulent economic climate, it's vital to fully exploit systems data. Your systems are no longer an overhead, they are a valuable asset. Used correctly, the information they contain helps reduce costs and increase revenues. The examples above provide some insight into what you could expect from a Business Opportunity Assessment. Each project is a ring-fenced, fixed-price assignment with agreed actionable deliverables. You will know what will be achieved and when you can expect the results. Balancing these with a fixed fee, you can judge project success and potential return on investment.

Would you like to better understand your customers and increase sales? If that's a 'Yes', do get in contact (details top right of page). We would be delighted to talk to you.

Not ready yet? Perhaps view a client project (any on the right of this page) to find out more about the results and benefits they achieved.