Winning Retail Pricing: 5 steps



Pricing is one of the most important functions of retail, which directly influences revenue. According to RSR, it’s a “fair” price that makes one retailer more attractive to consumers than other. What is the way to set it up and make buyers believe a shop has the best prices, taking into account the 50% markup a retailer needs to survive?

The final global goal of any business is getting profit and increasing it in different ways like marketing strategy building, inventory optimization, cost minimization, pricing perfection etc.

To maximize revenue and profit, a retailer needs to go through different stages from data collection and data processing workflow implementation to retail pricing strategy building and goals execution.

This path is concluded by reaction to the outcome and decision making based on predictive analytics. See how the winning retail path looks like with its main challenges and solutions.

1
Data collection and processing

Depending on the business scale, the first step on the winning retail path consists of:

• Competitive data collecting and processing to build competitive-based pricing
• Inner data processing to set up a cost-based pricing strategy

Data processing is sustained by qualitative data, high data delivery speed and timeliness, and availability to the final user (e.g. a category managers).

Obstacles & Challenges

1) The most difficult issue is getting high-quality data regarding the competitors. The indicators of quality: high level of the comparison coverage, low number of zero price, low percentage of errors, etc.

2) Data processing shouldn’t be perplexed by complicated user interface and data disintegrated in many spreadsheets and systems.

3) Price consistency should be achieved in all channels.

Solution

A price comparison software providing retailer with: 95% qualitative data

- high data delivery speed (2 hours at maximum to send the data to the internal system of retailer)

- timeliness (if one needs to get the data everyday at 8 a.m., it should be at his disposal at 8 a.m strict)

2
Data analysis

The process of transforming raw data into useful information through a variety of techniques is called data analysis.

The usefulness of information is estimated by the alerting opportunities it unseals: finding deviations, anomalies, tendencies for further development of strategies.

The data analysis should be lead quickly and on time, pricing decisions should be taken instantly, if not — they won’t be relevant to the current situation.

Obstacles & Challenges

Poor data comprehensibility, time-consuming. Category managers spend substantial part of their day updating and analysing data several times per day to get a complete picture, which easily makes the data outdated.

Instant data analysis should be performed to meet the increased aggressiveness of competitors (which is among TOP-3 business challenges)

Solution

Price monitoring service allowing access to any required type of data and report in just 3 clicks, which reduces repricing time from 30 minutes to 5-7 minutes per single product category

User-friendly interface for category manager to easily analyze data and make decisions with minimum technical knowledge



3
Goals & Strategies

When growing, retailers start developing their own strategies and setting goals based on their experience of data analysis and current priorities.

They launch product segmentation, determine willingness to pay and configure retail prices for each of this segment. They choose KVI-positions, items helping to protect market share, etc.

They’re labeling every segment (e.g. the one which drives traffic, the one which affects company image) and set different pricing programs for every label, defining the challenges. The pricing programs are bound with the company pricing strategies.

Obstacles & Challenges

Challenge 1. To closely connect the pricing activities to the overall company strategy.

Challenge 2. To develop an extensive library of pricing rules taking into account all nuances.

Challenge 3. To choose the right pricing program for every label by going through many experiments and testing many different ways to increase profit.

Solution

A tool able to predict the outcome of all types of pricing decisions, simulating them in order to choose the most reasonable moves towards optimal (i.e. profitable) pricing.


4
Automation

The next step in the evolution of a retailer is the automation of the pricing decision-making process and designing the process of automatic changes in the repricing process regarding the market changes.

The decisions effectiveness should be quickly measured, the programs and repricing rules should be quickly updated.

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Obstacles & Challenges

The main challenge here is adjustment to the automation technology without technical skills.

Solution

A fine-tuned price optimization software that automates pricing regarding the company strategies and pricing programs, and alerts a retail manager about anything outside of them.

This symbiosis of software and manager leads to the most optimal retail pricing.


5
Performance Optimization

At this stage the price optimization has predictable results through the pricing model crafting, training, testing, tuning. It means that the impact of every repricing action is thoroughly measured and tested, the demand changes are forecasted.

Obstacles & Challenges

The complexity of model building, testing and tuning restrains many retailers from getting immediate insights and optimizing their performance by actually seeing the effects of their decisions.

Solution

A sophisticated software using science, machine learning, price simulation and analytics to deliver the best working models, to predict the future market changes and let the retailer take a profit of them, rather than being taken by them.


The Competera pricing platform provides you with all tools to follow the steps:
• an advanced library of pricing rules, alerts, reports, and the results sent via API;
• tailored pricing programs, testing, and Guidance hub;
• price elasticity calculation, prices per segment, predictive pricing.