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Retail changing dynamics | Post-Covid-19 shifting consumer mindsets, marketing data and intelligent analysis

Posted | Updated by Insights team:
Dr. Evangelo Damigos; PhD | Head of Digital Futures Research Desk
  • Competitive Differentiation
  • Sustainable Growth and Tech Trends
  • Post-Covid-19


Publication | Update: Sep 2020

The retail industry is undergoing unprecedented changes, redefining retail marketers’ understanding of consumer behavior.

One change is obvious: Shopping experiences for today’s consumers are no longer defined by traditional brick-and-mortar. Instead, retailers are rapidly shifting from offline experiences to primarily online experiences, aimed at satisfying consumer expectations for at-home shopping convenience and guidelines.

Retail has reinvented itself multiple times over the past couple of centuries, and now more than ever, we’ve evolved into a new age of modern shopping. Consumers expect retail brands to meet them wherever they are, without boundary.

Meanwhile, the proliferation of digital technology means that consumers have more access than ever to product information and reviews that ultimately impact their purchasing decisions. As a result, retail marketers are tapping into more and more data sources and channels to give customers the relevant, engaging, flexible experiences that they expect throughout the shopping journey. This retail industry transformation presents a real challenge for retail marketers, but also an extraordinary opportunity. It’s never been more important for retail marketers to understand the changing dynamics of the industry and how to best reach their customers, while also driving efficiency across their entire marketing budget.

Marketers have struggled to keep up with changing customer preferences in recent months, as consumer confidence levels have fluctuated and behaviour has changed at a rapid pace.

Shifting consumer mindsets during the pandemic mean brands need to take action on data in real time, but this is only possible when silos are broken down.

According to Steve Hemsley, Marketing Week author, Real-time data is more important than ever in an increasingly virtual marketing world, to ensure campaigns resonate with customers right now. However, according to a Salesforce Datorama’s ‘Marketing Intelligence Report’, 80% of marketers do not have access to daily or real-time data reports.

Brands can use data and measurement to evaluate how each message and tactic is resonating, but it is crucial siloed data sources are unified to give consumers what they want.

The Datorama report reveals that 42% of marketers are still operating in silos and measuring performance independently within each tool or platform, which makes it hard to react quickly.

Impact on retail

The retail industry has been particularly affected by the pandemic, with social distancing regulations and the mandatory closure of physical stores forcing operators to make rapid moves to ecommerce.

Consumers are reporting that interactions with retailers’ products, services, and brands across touchpoints are disconnected, with only 13% of consumers saying companies generally excel at delivering connected experiences. Meanwhile, brands rated the biggest consumer challenges as engagement and discovery (32%) and awareness and acquisition (24%).

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As they look to the future, retail brands are attempting to focus their investments and resources in the right areas, aiming to double down on messages that drive top-of-the-funnel traffic.

It’s clear that retail brands need to improve their connections with customers, but how will they get there?

They can start by identifying and understanding some of the underlying pain points.

Marks & Spencer International has been utilising products across the Salesforce ecosystem including Salesforce Commerce Cloud, as well as Salesforce Marketing Cloud products such as Datorama. With Datorama, M&S International has been able to gain a better understanding of customer behaviours and seamlessly connect its data, to gain efficiency and greater value.

“There have been massive shifts from in-store to online shopping, a downturn in some product categories versus growth in others, cancelled summer holidays and a rapidly changing competitive landscape,” says M&S International’s senior digital marketing manager, Matthew Johnston. “All have contributed to a volatile retail environment. Meanwhile marketing budgets are more heavily scrutinised than ever before.”

M&S International has taken on an extremely agile test-and-learn mentality, focusing on a few key areas: visibility and controls on its spending, an understanding of its cross-channel metrics, and resource efficiency.

Datorama does the heavy lifting in terms of connecting and integrating M&S International’s data, so the retailer’s international marketing team has the confidence to make tough decisions as the business moves from a stage of resilience to recovery.

“We have combined our marketing data across Facebook, Google, influencer and affiliate programmes, plus display advertising to gain more of a holistic view of all our marketing activities,” says Johnston. “This helps us to set and forecast budgets and monitor spend.”

There have been massive shifts from in-store to online shopping, a downturn in some product categories versus growth in others, cancelled summer holidays and a rapidly changing competitive landscape.

Matthew Johnston, M&S International

During the pandemic, every retail brand has to build strong relationships with customers across their omnichannel journey by connecting and analysing data to gather deeper insight. However, this has not been easy for some.

Research by Salesforce reveals 55% of retailers struggle to establish relationships with customers in normal times because they are unable to turn data into insights. Their messaging becomes disjointed and lacks authenticity.

The solution is to automate data integration and management and use marketing intelligence platforms that offer cross-platform and cross-channel analytics to deliver instant data visualisation and intelligent recommendations.

Thinking differently

For example, now could be the time for a brand to shift their media mix. Perhaps they should move some of their budget from billboard advertising to video streaming services or mobile gaming if this is where customers are spending more of their time?

Noble Foods, which owns the Happy Egg Co brand, brought forward much of its marketing spend from the winter to this summer and switched budget from in-store to television during lockdown. TV viewing figures were higher than normal and there were media deals to be had. The timing made sense as the government was encouraging people to make sure they were getting enough vitamin D. The ingredients in the Happy Egg Co’s bird feed means its eggs have 28% more vitamin D than other eggs.

“Around 95% of households buy eggs and we managed to turn the campaign around in six weeks, sharing data across agencies and using platforms such as Microsoft Teams to share insights and ideas,” says head of marketing Matt Davis. “We also spent more on social media and working with influencers.”

Indeed, social media usage has soared during the pandemic as people give their views on the political, social and health issues of the day. Customers are quick to tweet about a negative experience with a business or post a picture of a product on Instagram if a brand has made them happy.

Marketers need to understand current brand sentiment and use social listening platforms to gain an overview of customer feedback, to see whether campaigns are resonating with the target audience.

Marketers can note how many times their brand or business is mentioned and use sentiment analysis to monitor positive references and flag negative ones. When social listening data is harmonised with performance across other media through marketing intelligence platforms, the marketing team has a more complete view of brand health.

Ultimately by monitoring sentiment, the target audience informs the marketing strategy at a time when consumer behaviour is less predictable.

Ravi Parmeswar, VP of consumer business intelligence at Johnson & Johnson Consumer Health, says its marketing and R&D teams have been busy harnessing data, including social listening data, to gather deep consumer insights.

“Data is always at the forefront of our decision making,” says Parmeswar. “Data analytics is an asset we leverage to identify insights and accelerate decision-making to unlock growth.”

Early on in the pandemic the company’s Listerine brand team noted an uptick in conversation on social media, speculating about the ability of the mouthwash to fight Covid-19.

“The science does not support this and, based on the social listening data showing confusion and misinformation, we moved quickly to update the owned channels to dispel myths and provide the facts that Listerine mouthwash has not been tested against any strains of coronavirus,” says Parmeswar.

He adds that data enables brands to be consumer-obsessed to better understand people’s needs, preferences and unique experiences in difficult times. “Be curious, be agile, and test and learn,” he says. “Leverage real-time data to drive effective decision-making and embrace an ROI mindset across all consumer touchpoints, whether that is ecommerce, media or promotional activities.”

Taking a data-driven approach also means brand messaging is more likely to fit the tone of today’s economic and health conversation. Examples of brands getting the pitch right include Nike, which was one of the first to promote the social distancing message. Coca-Cola and McDonald’s even adjusted the letter spacing in their logos to emphasise the importance of safe distances.

Brands are realising that in such strange times they will only unlock short- and long-term growth if they tune into their customer community, and adapt to changing behaviours by prioritising empathy and trust when communicating.  They can only do this effectively if they use data and measurement to evaluate the resonance of every message they convey and tactic they employ.

Download the ‘Marketing Intelligence for Retail Marketers’ report here

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Objectives and Study Scope

This study has assimilated knowledge and insight from business and subject-matter experts, and from a broad spectrum of market initiatives. Building on this research, the objectives of this market research report is to provide actionable intelligence on opportunities alongside the market size of various segments, as well as fact-based information on key factors influencing the market- growth drivers, industry-specific challenges and other critical issues in terms of detailed analysis and impact.

The report in its entirety provides a comprehensive overview of the current global condition, as well as notable opportunities and challenges. The analysis reflects market size, latest trends, growth drivers, threats, opportunities, as well as key market segments. The study addresses market dynamics in several geographic segments along with market analysis for the current market environment and future scenario over the forecast period. The report also segments the market into various categories based on the product, end user, application, type, and region.
The report also studies various growth drivers and restraints impacting the  market, plus a comprehensive market and vendor landscape in addition to a SWOT analysis of the key players.  This analysis also examines the competitive landscape within each market. Market factors are assessed by examining barriers to entry and market opportunities. Strategies adopted by key players including recent developments, new product launches, merger and acquisitions, and other insightful updates are provided.

Research Process & Methodology

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We leverage extensive primary research, our contact database, knowledge of companies and industry relationships, patent and academic journal searches, and Institutes and University associate links to frame a strong visibility in the markets and technologies we cover.

We draw on available data sources and methods to profile developments. We use computerised data mining methods and analytical techniques, including cluster and regression modelling, to identify patterns from publicly available online information on enterprise web sites.
Historical, qualitative and quantitative information is obtained principally from confidential and proprietary sources, professional network, annual reports, investor relationship presentations, and expert interviews, about key factors, such as recent trends in industry performance and identify factors underlying those trends - drivers, restraints, opportunities, and challenges influencing the growth of the market, for both, the supply and demand sides.
In addition to our own desk research, various secondary sources, such as Hoovers, Dun & Bradstreet, Bloomberg BusinessWeek, Statista, are referred to identify key players in the industry, supply chain and market size, percentage shares, splits, and breakdowns into segments and subsegments with respect to individual growth trends, prospects, and contribution to the total market.

Research Portfolio Sources:

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  • Dun & Bradstreet: Country Reports, Country Riskline Reports, Economic Indicators 5yr Forecast, and Industry Reports

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  • Ken Research: Industry Reports, Regional Industry Reports and Global Industry Reports

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  • OECD: Economic Outlook, Economic Surveys, Energy Prices and Taxes, Main Economic Indicators, Main Science and Technology Indicators, National Accounts, Quarterly International Trade Statistics

  • Oxford Economics: Global Industry Forecasts, Country Economic Forecasts, Industry Forecast Data, and Monthly Industry Briefings

  • Progressive Digital Media: Industry Snapshots, News, Company Profiles, Energy Business Review

  • Project Syndicate: News Commentary

  • Technavio: Global Market Assessment Reports, Regional Market Assessment Reports, and Market Assessment Country Reports

  • The Economist Intelligence Unit: Country Summaries, Industry Briefings, Industry Reports and Industry Statistics

Global Business Reviews, Research Papers, Commentary & Strategy Reports

  • World Bank

  • World Trade Organization

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M&A and Risk Management | Regulation

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Forecast methodology

The future outlook “forecast” is based on a set of statistical methods such as regression analysis, industry specific drivers as well as analyst evaluations, as well as analysis of the trends that influence economic outcomes and business decision making.
The Global Economic Model is covering the political environment, the macroeconomic environment, market opportunities, policy towards free enterprise and competition, policy towards foreign investment, foreign trade and exchange controls, taxes, financing, the labour market and infrastructure. We aim update our market forecast to include the latest market developments and trends.

Forecasts, Data modelling and indicator normalisation

Review of independent forecasts for the main macroeconomic variables by the following organizations provide a holistic overview of the range of alternative opinions:

  • Cambridge Econometrics (CE)

  • The Centre for Economic and Business Research (CEBR)

  • Experian Economics (EE)

  • Oxford Economics (OE)

As a result, the reported forecasts derive from different forecasters and may not represent the view of any one forecaster over the whole of the forecast period. These projections provide an indication of what is, in our view most likely to happen, not what it will definitely happen.

Short- and medium-term forecasts are based on a “demand-side” forecasting framework, under the assumption that supply adjusts to meet demand either directly through changes in output or through the depletion of inventories.
Long-term projections rely on a supply-side framework, in which output is determined by the availability of labour and capital equipment and the growth in productivity.
Long-term growth prospects, are impacted by factors including the workforce capabilities, the openness of the economy to trade, the legal framework, fiscal policy, the degree of government regulation.

Direct contribution to GDP
The method for calculating the direct contribution of an industry to GDP, is to measure its ‘gross value added’ (GVA); that is, to calculate the difference between the industry’s total pre­tax revenue and its total bought­in costs (costs excluding wages and salaries).

Forecasts of GDP growth: GDP = CN+IN+GS+NEX

GDP growth estimates take into account:

  • Consumption, expressed as a function of income, wealth, prices and interest rates;

  • Investment as a function of the return on capital and changes in capacity utilization; Government spending as a function of intervention initiatives and state of the economy;

  • Net exports as a function of global economic conditions.

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Market Quantification
All relevant markets are quantified utilizing revenue figures for the forecast period. The Compound Annual Growth Rate (CAGR) within each segment is used to measure growth and to extrapolate data when figures are not publicly available.

Revenues

Our market segments reflect major categories and subcategories of the global market, followed by an analysis of statistical data covering national spending and international trade relations and patterns. Market values reflect revenues paid by the final customer / end user to vendors and service providers either directly or through distribution channels, excluding VAT. Local currencies are converted to USD using the yearly average exchange rates of local currencies to the USD for the respective year as provided by the IMF World Economic Outlook Database.

Industry Life Cycle Market Phase

Market phase is determined using factors in the Industry Life Cycle model. The adapted market phase definitions are as follows:

  • Nascent: New market need not yet determined; growth begins increasing toward end of cycle

  • Growth: Growth trajectory picks up; high growth rates

  • Mature: Typically fewer firms than growth phase, as dominant solutions continue to capture the majority of market share and market consolidation occurs, displaying lower growth rates that are typically on par with the general economy

  • Decline: Further market consolidation, rapidly declining growth rates

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The Global Economic Model
The Global Economic Model brings together macroeconomic and sectoral forecasts for quantifying the key relationships.

The model is a hybrid statistical model that uses macroeconomic variables and inter-industry linkages to forecast sectoral output. The model is used to forecast not just output, but prices, wages, employment and investment. The principal variables driving the industry model are the components of final demand, which directly or indirectly determine the demand facing each industry. However, other macroeconomic assumptions — in particular exchange rates, as well as world commodity prices — also enter into the equation, as well as other industry specific factors that have been or are expected to impact.

  • Vector Auto Regression (VAR) statistical models capturing the linear interdependencies among multiple time series, are best used for short-term forecasting, whereby shocks to demand will generate economic cycles that can be influenced by fiscal and monetary policy.

  • Dynamic-Stochastic Equilibrium (DSE) models replicate the behaviour of the economy by analyzing the interaction of economic variables, whereby output is determined by supply side factors, such as investment, demographics, labour participation and productivity.

  • Dynamic Econometric Error Correction (DEEC) modelling combines VAR and DSE models by estimating the speed at which a dependent variable returns to its equilibrium after a shock, as well as assessing the impact of a company, industry, new technology, regulation, or market change. DEEC modelling is best suited for forecasting.

Forecasts of GDP growth per capita based on these factors can then be combined with demographic projections to give forecasts for overall GDP growth.
Wherever possible, publicly available data from official sources are used for the latest available year. Qualitative indicators are normalised (on the basis of: Normalised x = (x - Min(x)) / (Max(x) - Min(x)) where Min(x) and Max(x) are, the lowest and highest values for any given indicator respectively) and then aggregated across categories to enable an overall comparison. The normalised value is then transformed into a positive number on a scale of 0 to 100. The weighting assigned to each indicator can be changed to reflect different assumptions about their relative importance.

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The principal explanatory variable in each industry’s output equation is the Total Demand variable, encompassing exogenous macroeconomic assumptions, consumer spending and investment, and intermediate demand for goods and services by sectors of the economy for use as inputs in the production of their own goods and services.

Elasticities
Elasticity measures the response of one economic variable to a change in another economic variable, whether the good or service is demanded as an input into a final product or whether it is the final product, and provides insight into the proportional impact of different economic actions and policy decisions.
Demand elasticities measure the change in the quantity demanded of a particular good or service as a result of changes to other economic variables, such as its own price, the price of competing or complementary goods and services, income levels, taxes.
Demand elasticities can be influenced by several factors. Each of these factors, along with the specific characteristics of the product, will interact to determine its overall responsiveness of demand to changes in prices and incomes.
The individual characteristics of a good or service will have an impact, but there are also a number of general factors that will typically affect the sensitivity of demand, such as the availability of substitutes, whereby the elasticity is typically higher the greater the number of available substitutes, as consumers can easily switch between different products.
The degree of necessity. Luxury products and habit forming ones, typically have a higher elasticity.
Proportion of the budget consumed by the item. Products that consume a large portion of the consumer’s budget tend to have greater elasticity.
Elasticities tend to be greater over the long run because consumers have more time to adjust their behaviour.
Finally, if the product or service is an input into a final product then the price elasticity will depend on the price elasticity of the final product, its cost share in the production costs, and the availability of substitutes for that good or service.

Prices
Prices are also forecast using an input-output framework. Input costs have two components; labour costs are driven by wages, while intermediate costs are computed as an input-output weighted aggregate of input sectors’ prices. Employment is a function of output and real sectoral wages, that are forecast as a function of whole economy growth in wages. Investment is forecast as a function of output and aggregate level business investment.

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