To the Stars
Key Outcome:
To give key users (management team) the information they need, when they need it, to make the best, most timely decisions so the company can achieve its strategic goals.
System Project Lifecycle:
Define – Design – Construct – Test – Train – Convert ( – Operate – Evaluate – Improve)
10 Stages: [Read more…] about Designing Better Management Information Systems 2
Leverage Risk Metrics
The greatest risk to a company’s ability to survive is when it owes money to creditors or lenders. Here, its ability to pay back those creditors must be viewed as a critical metric for the company to monitor, particularly given various uncertainties the company may face in the current economic climate. Clearly a business which is cash-rich and carries no debt is in a much happier and healthier (less risky) situation, although even here it should still monitor its cash inflows and outflows and carefully manage its working capital cycle. But a company which owes money to others – especially when it is also waiting on receipts due from its own debtors/customers is in a far riskier position. [Read more…] about Risk Metrics 2
Profitability Metrics allow a company to maximise efficiency thereby minimising costs (for a given level of sales) and improve overall profitability.
Inventory Metrics
Generally in traditional manufacturing or retail businesses, the largest percentage of costs relative to sales is taken up by costs of the goods being sold. Before selling, the goods will be sitting on the company’s Balance Sheet as Inventory (which will usually be valued at the cost paid to make or buy that product) and likewise the physical product itself will be sitting around either on a shelf in a shop, in a factory or held in a warehouse or distribution centre. [Read more…] about Profitability Metrics 2
Terms Related to Matching and Ranking
Ranking (a function which selects and returns results ordered based on some kind of relevancy algorithm, e.g. synonym matching to product database)
Two-step ranking (as above but with two stages involved in the selection and ranking process, e.g. synonym matching followed by sales best-seller ranking)
Text string (e.g. the search query typed into the search box in an online library catalogue or a book-seller’s website)
Matching (e.g. matching the search text string to a pre-defined list of categories in the database of books held/sold)
Controlled vocabulary index (where there are only a certain number of words which can actually match like for like, e.g. a subject index for all the books in a library, or all the books for sale on a website)
Synonym matching (where matching will be done not only on the exact words in the text string but also relevant synonyms for the exact words searched, e.g. where searches in a bookseller’s website returns relevant matches ranging across a number of possible/similar categories)
Parent category (e.g. high-level subject index)
Child category (e.g. sub-topics within the high-level subject index)
Tree of categories (the ordered list of parent and child categories)
Relevancy ranking (ranked according to some pre-defined relevancy algorithm, likely based upon both synonym matching and best-seller ranking)
Terms Related to Selling
Best-seller ranking (best-seller = as defined for all those products falling within a specific sub-category only; ranked according to highest levels of sales within specific sub-category)
Co-occurring sales (sales orders for multiple products placed by one customer in one order)
Co-occurrence sales data (all data related to multiple sales orders being placed by any one customer at any one time)
Co-occurrence matrix (all the co-occurrence data mapped out structurally to allow better analysis and sales-optimising decision-support)
A/B (split) testing (testing two different but similar options to different customer cohorts simultaneously to gather empirical evidence as to which option drives more sales)
Terms Related to Browsing (but not Selling)
Co-occurrence clickstream data (all data related to multiple views/clicks/browses by any one customer during their current visit that results in the sale of at least one book to that customer, i.e. what other products have you viewed together with the product you went on to purchase, even though you didn’t actually buy the viewed product) (e.g. Amazon “What other items do customers buy after viewing this item” recommendations)
Also mapped to a Co-occurrence clickstream matrix
Mapping the Above to a Product Database
Each item in a product database should have the following defined fields:
The full tree of categories the product fits into, including:
– All relevant high-level subject categories
– All relevant sub-topics within those high-level categories
Sales best-seller ranking
Cross-referencing categories (e.g. genre, author, year of publication, etc.)
Up-selling / cross-selling recommendations (e.g. Amazon’s “frequently bought together” and “customers who bought this items also bought” recommendations)
Advanced databases may include possible cross-sell / up-sell discount %’s to offer when making the recommendations to encourage sales (Note: % discount levels to be offered on cross-/up-sell will be dependent on empirical review of selling metrics for those products i.e. whether or not offering a % discount increases sales volumes or not. It may be subject to frequent change dependent on season, customer type, etc.. As such, the business may not wish to hard-code recommended % discounts into the product database, or if they do so, to have functionality to allow regular review and rapid updating in order to optimise sales.)
Such up-/cross-selling metrics should be tracked at all times (e.g. what products do customers buy together – from co-occurrence sales data; what has driven that buying behaviour; what impact do different marketing offers and % discounts have on sales volumes and values; what marketing offers and % discounts should be provided to optimise sales values; what other products do customers view when they buy this product – from co-occurrence clickstream data;). The most up-to-date metrics should be maintained – likely an independent database table which regularly feeds into the product database – to allow automation of the sales optimisation process.
Hence product database for each product will be dynamically updated for latest data on:
1. Best recommendation for up-sell/cross-sell, with or without sales optimising % discount offer
2. What other products are purchased when this product is purchased
3. What other products are purchased when this product is viewed
Summarised as:
Frequently bought together metric, possibly with addition of sales-optimising % discount
Recommendation engines (one based on multiple purchases made by others based on this product purchase, another based on single purchases made by others based on this product viewed)
Sales Metrics look at how well (or badly) the business is identifying potential customers, engaging them or capturing their attention, communicating to them about its products or services and marketing offers, and then convincing them to buy its products or services. This process of finding and converting new customers is referred to as the Sales Funnel, although the exact process will vary dependent on the business or industry, the product or service being sold, and the type of customer being sold to. [Read more…] about Sales Metrics 2
PROFIT & LOSS ACCOUNT
Unit sales
Value sales (before discounting, net of discounts)
Variable costs – raw materials
Variable costs – inbound delivery, labour, packaging, fulfilment
Total variable costs
Fixed costs – salaries, overheads, premises costs (rent/mortgage, utilities)
Profit before non-cash costs [Read more…] about MIS: Elements of a Management Information System
Marketing Analytics uses data (marketing/sales/order data) to describe, explain and predict customer behaviour. It allows management to make better business decisions, to optimise marketing tactics and strategies, improve the company’s results and achieve its goals.
There are three kinds of analytics: descriptive, predictive and prescriptive. [Read more…] about Functional Analytics: Marketing