Traffic, conversion, average check: myths and rules

Researches of data sets resemble treasure hunts: it is not clear who, where, what and why looks for but an uncanny turmoil is everywhere. Businesspersons request applicable knowledge, mathematicians ask for problem formulation, mediators commute between them hoping earn a “round-trip ticket”.

Anticipations of innumerable treasures spark endeavours of disorderly knowledge acquisition. Gold rush foams attract laymen with a hope to fill pockets. The market spreads rumours fanning the flames of vanity. Conferences, business lunches and “last suppers” make scoundrels come to light.

Irrelevant market consultants, employees-defectors, interpreters of others’ achievements broadcast a contagion of odd theories. The market absorbs fake regularities like an audacious gold digger that takes a rabbit’s foot in place of a pickaxe in the pit and substitutes four-leaf clover for a panning tray.

Spontaneous predatory approaches deplete trust and compromise gold diggers. Piles of gangue, scrolls of inapplicable equations and artefacts of lingering mischance discourage initiates. Wee advances are posed as heroic victories, failures are justified as gains of experience.

Expeditions to prominent brands’ peaks, valleys and canyons of desperate discounters enabled me to extract nuggets in large amounts that boost effectiveness of retail by a few basis points each but did not managed to increase an edition – rules do not work out for similar businesses.

The matter is not in brands’ uniqueness, regional peculiarities and clients’ features but in exclusive combinations of far more various range of factors. Let us consider general rules and dispel treacherous myths.

Traffic. Variables that matter: area of a shop, cluster and location: in a mall or it has a separate entrance from the street. Difference between client flows on floors in a shopping centre is not statistically significant.

While resettling luxury goods to second floors and descending daily goods to the first ones – a change in traffic does not compensate for a rent rate difference. The more is the area of a shop the higher is the traffic. In shopping malls, the volume of incoming flow is greater than in separate street shops.

Conversion. The ordinal number of a storey is found on the border of statistical significance – you can boldly toss a coin while making a decision. Conversion rate depends on a cluster structure of incoming populations.

Conversion is higher in street shops and inversely proportional to traffic: the greater is a client inflow the lower is purchase probability. Average customers and impulsive shoppers increase conversion, adepts and buyers of outwear reduce it.

Average check. Depends on region, structure of client inflows, cluster and location of a shop. The younger is a site (considering latest opening, finish of a maintenance or rebranding date) the higher is the size of an average check.

Stores with a separate entrance have a greater average check comparing to those situated in malls. Novices and snobs increase average check while shoppers and long-term customers decrease it.

Price of goods. Conversion rate, traffic and a region matter. There is no difference between client clusters. The highest prices are in new shops, the lowest – in the flagship ones. High conversion leads to growth of an average price.

Number of products in a check. Region, cluster, store, location are statistically significant. Customer clusters do not differ in the quantity of goods in a check.

Street shops sell more per visit than stores in malls. The higher is conversion of flows – the more are there items in a check. Shoppers, tired and average clients amplify the number of purchases per visit, adepts and snobs – cut it.

Customer clusters. Impact all indicators without exceptions. The greater a number personified purchases that are recognized by identification tools the more manageable is forecast execution.

Shop cluster. Influences traffic: the higher is a rank – the greater is an incoming flow. Dependence of conversion is similar but is weaker. Inverse rules: the lower is the cluster of a shop – the higher are average check and price.

Product clusters. Freshness of a fashion, novelty of a collection, weather dependence, share of basic stock – factors that affect purchases of an inflow. There are no wonders here.

Seller clusters. Decision-making torments, doubts about felicity of a deal – psychological determinants of customers’ anxiety, which they expect sales clerks to smooth. Training and levelling of vendor clusters to match the structure of client flows increase a full range of indicators.

Area of a shop. Influences traffic: the larger is a shopping area the more customers are there. It changes the activity level: flagman shops lower conversion rate. Does not affect other indicators.

Location. Impacts traffic: if situated in the street customer flows are inferior to those in a shopping centre. Customer conversion of street shops is higher than of stores in malls. Average check is greater for street. Average price does not vary. The number of purchases in a check is larger for street.

Floor. Does not affect effective level of traffic considering the difference coefficient in a rent rate. The higher is a floor the lower is the conversion. Size of check, price and number of items do not depend on the floor where a shop is situated.

Management according to goals. Sorted lists of regions, towns, shops, goods, vendors point to mutual success but do not provide tools to impact. A target sales model enables to calculate indicators of shopping locations via the structure of customer flows, evaluate the potential and track a dynamics of success.

Request depersonalized presentation of previous projects to familiarize yourself the way alluvial gold of new knowledge looks like before submitting information to Big Data specialists. This will help level off expectations and detect traps – do not swallow pica and pyrite purported to be precious metals.

In a battle iron is of more value than gold – you had better pan rules of sustainable business development rather than seek out for the philosopher’s stone of an instant miracle.

Author: Oleg Braginsky

Translation: Temir Barakov and Daniil Shmitt

Source: New Retail