Marketing strategy of Using Simulated Experience to Make Sense of Big Data

Posted by Zander Henry on Aug-22-2018

1. The vision of Using Simulated Experience to Make Sense of Big Data

The vision of Using Simulated Experience to Make Sense of Big Data is to be the leading quality service and product provider for customers. Being the best and the leading player means that Using Simulated Experience to Make Sense of Big Data marketing strategy and operations focus on:

• Providing high quality of products and services
• Providing value to customers
• Concentrate on building customer experience

2. The mission of Using Simulated Experience to Make Sense of Big Data

Using Simulated Experience to Make Sense of Big Data marketing strategy is grounded in its mission. The mission for Using Simulated Experience to Make Sense of Big Data is to be the favorite brand of the customers. This mission is essential for the marketing strategy of Using Simulated Experience to Make Sense of Big Data as it focuses on all operations and marketing activities in the direction of:

• Consumer centrism
• Using research to understand and influence consumers

3. Brand Equity of Using Simulated Experience to Make Sense of Big Data

Understanding and knowing the brand equity is vital for directing and giving meaning to the marketing strategy of Using Simulated Experience to Make Sense of Big Data. The knowledge of brand equity will help in shaping Using Simulated Experience to Make Sense of Big Data marketing strategy effectively – thereby facilitating the growth of business for Using Simulated Experience to Make Sense of Big Data.

3.1. Brand awareness

• Using Simulated Experience to Make Sense of Big Data has high brand awareness because of international operations
• The company focuses on higher budget allocation in the country of origin
• Each market for Using Simulated Experience to Make Sense of Big Data has modified marketing and strategic directives and plans

3.2. Brand association

• Using Simulated Experience to Make Sense of Big Data is directly associated with the brand name and product category
• Using Simulated Experience to Make Sense of Big Data has a broad product portfolio
• Using Simulated Experience to Make Sense of Big Data is associated with promising and delivering quality and innovative products
• Using Simulated Experience to Make Sense of Big Data is also associated with excellent customer service

3.3. Brand loyalty

• Using Simulated Experience to Make Sense of Big Data has been successful at gaining high consumer loyalty because of unique and influential marketing strategy
• Using Simulated Experience to Make Sense of Big Data has a global customer base
• Using Simulated Experience to Make Sense of Big Data keeps adding value addition to the products and product portfolio to keep consumers engaged

3.4. Brand asset

• Using Simulated Experience to Make Sense of Big Data has a substantial brand value
• Using Simulated Experience to Make Sense of Big Data also enjoys the high financial worth
• Using Simulated Experience to Make Sense of Big Data focuses on building a reliable and robust employee base

3.5. Brand element

• Using Simulated Experience to Make Sense of Big Data uses the brand element as a means of competitive advantage
• Uses adaptability in product, services, and marketing to meet different cultural demands

4. Situational Analysis of Using Simulated Experience to Make Sense of Big Data

The situational analysis will help in developing the marketing strategy of Using Simulated Experience to Make Sense of Big Data by conducting a thorough market analysis. This market analysis will aid in understanding the compatibility between external opportunities and other factors, and internal strengths – to be used to maximize the marketing influence of Using Simulated Experience to Make Sense of Big Data.

4.1. SWOT

4.1.1. Strengths

Using Simulated Experience to Make Sense of Big Data marketing strategy can benefit from the following internal advantages:

• Strong brand image
• Global distribution network
• Investment in market research
• Innovation

4.1.2. Weakness

Using Simulated Experience to Make Sense of Big Data faces challenges in marketing strategy because of the following weakness:

• Slow organizational processes
• High product prices

4.1.3. Opportunity

Using Simulated Experience to Make Sense of Big Data has the following possibilities of business growth:

• Green lifestyles
• Regional expansion
• Diversification

4.1.4. Threats

Using Simulated Experience to Make Sense of Big Data faces business threats because of the following factors:

• Increased competition
• Increased imitation

4.2. PESTEL

4.2.1. Political

• Using Simulated Experience to Make Sense of Big Data operates I markets with political stability
• Using Simulated Experience to Make Sense of Big Data has funding support from the government for small businesses

4.2.2. Economic

• Using Simulated Experience to Make Sense of Big Data enjoys high sales because of higher GDP
• Lower interest rates make business expansion and loaning easier for Using Simulated Experience to Make Sense of Big Data
• Low inflation strengthens the financial position of Using Simulated Experience to Make Sense of Big Data

4.2.3. Social

• Higher education and awareness increases sales of Using Simulated Experience to Make Sense of Big Data predict
• Using Simulated Experience to Make Sense of Big Data focuses on understanding consumers and fulfilling their demands through its offerings

4.2.4. Environmental

• Using Simulated Experience to Make Sense of Big Data has an active CSR program
• Using Simulated Experience to Make Sense of Big Data ensures environmental safety in all its operations

4.2.5. Legal

• Using Simulated Experience to Make Sense of Big Data is aware of local and global laws of business and human resource management
• Using Simulated Experience to Make Sense of Big Data abides by all statutes – especially labour law, discrimination law, and employee safety laws

4.3. Porter’s Five Forces

4.3.1. Threat of substitutes

• High risk of replacements
• Substitutes offer similar products at low prices

4.3.2. The threat of new entrants

• New entrants need high financial investment
• New entrants need updated technology for keeping par with industry progress

4.3.3. Bargaining power of buyers

• Sales made to end consumer directly
• Stocking of products at retailers, as well as own-controlled retail outlets

4.3.4. Bargaining power of suppliers

• Multiple suppliers of raw materials
• Suppliers are chosen after careful inspection, and through contracts

4.3.5. Industry rivalry

• High industry rivalry
• Players offer similar products
• Players compete through marketing to influence consumers

5. Marketing Objectives for Using Simulated Experience to Make Sense of Big Data: The Marketing Strategy of Using Simulated Experience to Make Sense of Big Data

Using Simulated Experience to Make Sense of Big Data marketing strategy has the following objectives for the current financial year:

5.1. Increased market penetration

• Increase top of mind recall for Using Simulated Experience to Make Sense of Big Data brand and products by 30%
• Increase sales for Using Simulated Experience to Make Sense of Big Data by 40% by the third quarter of the financial year
• Achieve a trial rate for new products of 10% during the first quarter of the launch
• Increase consumption rate of existing products by 45% during the current financial year

5.2. Enhanced brand recognition

• Increase top of mind recall by 65% during the current fiscal year
• Increase brand recognition by 80% during the first two quarters of the current financial year

5.3. Increased use of digital marketing

• Acquire 25,000 new online customers during the financial year
• Increase website traffic through using blogging and email tactics effectively by 505 during the first two quarters of the year
• Acquire 65,000 likes on the official Facebook page of Using Simulated Experience to Make Sense of Big Data during the first quarter of the financial year

5.4. Retail Growth

• Contract with five more leading supermarkets in the first quarter of the year to stock product at eye level shelving
• Contract with two leading online retail sites – eBay and Amazon – to stock our products, and increase accessibility for consumers globally by the second quarter of the financial year

6. Segmentation of Using Simulated Experience to Make Sense of Big Data

Using Simulated Experience to Make Sense of Big Data marketing strategy uses different means of segmentation to reach an increase in market penetration.

6.1. Demographic segmentation

6.1.1. Age

Using Simulated Experience to Make Sense of Big Data has consumers of age groups

• 20-45 years
• 45-60 years

6.1.2. Gender

• Using Simulated Experience to Make Sense of Big Data has a broad product portfolio for both males and females

6.1.3. Life-cycle stage

Consumers for Using Simulated Experience to Make Sense of Big Data, according to the marketing strategy, are in the following various life cycle stages:

• Single students
• Single graduates
• Single people living at home/not living at home
• Young couples without children
• Married couples with one to four children – all at home
• Married couples with one or two children in college
• Old married couples with an empty nest

6.1.4. Occupation

The marketing strategy devises the following occupations for Using Simulated Experience to Make Sense of Big Data consumers:

• Professionals
• Students
• House makers

6.2. Psychographic segmentation

6.2.1. Social class

• Using Simulated Experience to Make Sense of Big Data focuses on segments of middle-upper and upper social classes

6.2.2. Lifestyle

Using Simulated Experience to Make Sense of Big Data consumer segments have the following lifestyle characteristics:

• They aspire towards a better and higher living standard
• They want to be successful – professionally and socially
• They are not hesitant to try new things, products and services in life
• They are confident in their behaviour and attitude
• They are mainstreamers in their fields

6.3. Geographic segmentation

6.3.1. Region

• Using Simulated Experience to Make Sense of Big Data has operations spread across the western developed countries such as America, the united kingdom, and the Netherlands
• It also has operations in emerging markets such as Brazil, India, and China

6.3.2. Density

• The focus of Using Simulated Experience to Make Sense of Big Data remains on the urban part of the population

6.4. Behavioural segmentation

6.4.1. Personality

The marketing strategy defines personality characteristics for the consumers of the brand of Using Simulated Experience to Make Sense of Big Data, such as:

• Determined
• Confident
• Ambitious
• Hardworking

6.4.2. Usage frequency

• The consumer segments for Using Simulated Experience to Make Sense of Big Data are regular and frequent users of the product

6.4.3. Benefits sought

• Consumers seek functional benefits
• The focus, however, is more on the emotional benefits reaped from the consumption of the brand

6.4.4. Degree of loyalty

• Consumers are very loyal
• Have an emotional attachment with the brand

7. Targeting of Using Simulated Experience to Make Sense of Big Data Positioning of Using Simulated Experience to Make Sense of Big Data

The marketing strategy of Using Simulated Experience to Make Sense of Big Data targets consumer groups based on segmentation as follows:

7.1. Target market

• The target market for Using Simulated Experience to Make Sense of Big Data is from middle to upper class
• The target market is ambitious and desires to purchase high-end consumer products
• This target market also seeks affordability
• To meet target market expectations, the Using Simulated Experience to Make Sense of Big Data focuses on quality control

7.2. Mass marketing

• The marketing strategy of Using Simulated Experience to Make Sense of Big Data focuses on mass marketing
• This also requires unique marketing designs and product promotion programs
• Using Simulated Experience to Make Sense of Big Data makes use of one strategy to influence all segments

7.3. Undifferentiated marketing strategy

• Using Simulated Experience to Make Sense of Big Data does not differentiate between market segments
• It uses a single marketing strategy to target all segments and consumer groups
• Based on this, Using Simulated Experience to Make Sense of Big Data also created the marketing mix under the marketing strategy as a singular one for the whole market – regardless of the segmentation divides.

7.4. Focus on quality

• Using Simulated Experience to Make Sense of Big Data has created, developed, and maintained a brand that satisfies all consumers under the undifferentiated marketing strategy and mass marketing
• No compromise on quality has been made in the broad product portfolio
• To ensure the influence of a single marketing strategy, the Using Simulated Experience to Make Sense of Big Data has also adopted a consumer-centric approach in its overall marketing strategy and operations as well
• This was used for targeting strategy as well as for maintaining growth

8. Company Competitive Advantage in the marketing strategy of Using Simulated Experience to Make Sense of Big Data

The marketing strategy of Using Simulated Experience to Make Sense of Big Data stands out from the clutter and competition. Using Simulated Experience to Make Sense of Big Data has also achieved a sustainable competitive advantage in its marketing strategy. This is because of the following factors that Using Simulated Experience to Make Sense of Big Data has utilized:

8.1. Cost-effectiveness

• Using Simulated Experience to Make Sense of Big Data focuses on reaching consumers effectively rather than grandeur
• Using Simulated Experience to Make Sense of Big Data focuses on developing an integrated marketing approach
• The use of digital marketing efficiently and expertly has helped the company reach a wider audience at a lower cost
• Using Simulated Experience to Make Sense of Big Data has in-house copywriters for marketing campaigns which also helps in controlling costs
• Using Simulated Experience to Make Sense of Big Data also focuses efforts on ground activities – which are less expensive than commercial marketing tactics

8.2. Innovation

• Using Simulated Experience to Make Sense of Big Data has stayed updated with latest developments in marketing research and marketing knowledge
• Using Simulated Experience to Make Sense of Big Data makes use of new and innovative tactics to reach its target consumers
• Using Simulated Experience to Make Sense of Big Data also employs top of the field marketers to facilitate its marketing strategy and promotional campaigns
• Each marketing campaign launched by Using Simulated Experience to Make Sense of Big Data is effective catchier and more influential than the previous one

8.3. Strong market research and consumer understanding grounded

• Using Simulated Experience to Make Sense of Big Data marketing strategy is strongly grounded in consumer and market research
• Using Simulated Experience to Make Sense of Big Data makes informed marketing campaigns and goals based on consumers’ behavioural feedback
• Using Simulated Experience to Make Sense of Big Data also incorporates consumer feedback in its marketing strategy
• Using Simulated Experience to Make Sense of Big Data marketing strategy is based on market trends, and consumer needs and wants

8.4. Making effective use of emotional appeals

• Consumers’ emotional needs strongly influence all marketing objectives and marketing goals set by Using Simulated Experience to Make Sense of Big Data
• In addition to fulfilling functional needs, Using Simulated Experience to Make Sense of Big Data also tries to fulfil the emotional and psychological needs of the consumer
• Using Simulated Experience to Make Sense of Big Data tries to build a strong emotional bond with the consumer, which also results in high consumer loyalty

9. Distribution Strategy of Using Simulated Experience to Make Sense of Big Data

Using Simulated Experience to Make Sense of Big Data marketing strategy highlights the use of the following distribution strategy to maximize reach and accessibility for consumers.

9.1. Intensive distribution strategy

• Using Simulated Experience to Make Sense of Big Data makes use of intensive distribution strategy because it is mass marketing
• Using Simulated Experience to Make Sense of Big Data’s marketing strategy is based on undifferentiated segments, and thus an intensive distribution strategy allows high penetration and reaches in the overall market
• With the use of the intensive distribution, Using Simulated Experience to Make Sense of Big Data tries to maximise its coverage of the markets where it's present
• For achieving the intensive strategy, the company uses hardcore 360-degree integrated marketing strategy and campaign to reach all consumers, across all segments in the market.

9.2. Direct distribution strategy

• Using Simulated Experience to Make Sense of Big Data uses direct distribution country of origin as well as in locations where it has subsidiary operations
• The Using Simulated Experience to Make Sense of Big Data also makes use of modern retailing channels
• Also, Using Simulated Experience to Make Sense of Big Data makes use of e-commerce and makes a sale through online retailers, as well as through the company website
• Direct distributions have allowed Using Simulated Experience to Make Sense of Big Data to increase market penetration and accessibility for consumers

9.3. Indirect distribution strategy

• This strategy is largely used for offshore operations where the Using Simulated Experience to Make Sense of Big Data does not have a subsidiary
• In these offshore locations, Using Simulated Experience to Make Sense of Big Data largely works through the export model
• This makes use of several intermediaries in between, before the product by Using Simulated Experience to Make Sense of Big Data reaches the target consumers
• Intermediaries for Using Simulated Experience to Make Sense of Big Data include not only the end retail outlets, but also sales agents, retail agents, and distribution agents in offshore locations

9.4. Selective distribution strategy

• For some products of its portfolio which are premium in nature, Using Simulated Experience to Make Sense of Big Data makes use of selective distribution channel
• Using Simulated Experience to Make Sense of Big Data has maintained a few outlets in the country of origin, and in selected offshore markets for these products
• These placements and locations are chosen based on the niche market that Using Simulated Experience to Make Sense of Big Data has for its premium products
• These locations, placements, and marketing strategy helps make the company’s product selectively, but readily accessible for its niche target audience

10. Competition Analysis in the marketing strategy of Using Simulated Experience to Make Sense of Big Data

The industry in which Using Simulated Experience to Make Sense of Big Data operates is very responsive to market and consumer trends. Using Simulated Experience to Make Sense of Big Data, therefore, needs to be vigilant in its market strategy towards competition – to make sure that it maintains its competitive advantage.

10.1. Strategic Group Analysis

• Using Simulated Experience to Make Sense of Big Data competes with direct and close competition based on quality and price
• Consumers choose between different companies from the industry based on their functional offering
• Consumers have progressively evolved to strengthen loyalty and form an emotional bond with products that they consume
• Using Simulated Experience to Make Sense of Big Data also competes, thereby, with close competition for building stronger brand image, increasing consume loyalty, and for forming strong emotional ties with the consumer

10.2. Industry rivalry

• Using Simulated Experience to Make Sense of Big Data experiences high industry rivalry
• The barriers to entry for the industry are low, and new entrants gain easy access in the industry
• The number of local as well as global players is increasing

11. Marketing mix of Using Simulated Experience to Make Sense of Big Data

The marketing mix for Using Simulated Experience to Make Sense of Big Data as per the marketing strategy is the following:

11.1. Product

• Using Simulated Experience to Make Sense of Big Data has a broad product portfolio
• Using Simulated Experience to Make Sense of Big Data provides mass marketed products for all segments across the market undifferentiated
• Using Simulated Experience to Make Sense of Big Data also provides some selected, premium products to niche customer groups
• All products in the portfolio consistently maintain high quality
• All products are tailored to meet consumer specifications, demands and needs across different regional markets
• The Using Simulated Experience to Make Sense of Big Data maintains a high focus on innovation in products and introduces new products frequently to keep the consumers engaged

11.2. Place

• Using Simulated Experience to Make Sense of Big Data wants to have a close, emotional and personal relationship with its consumers
• The company maintains high control in its distribution strategies – especially through direct distribution strategy
• The company has a presence in leading supermarkets
• The Using Simulated Experience to Make Sense of Big Data also has company-operated stores in malls, and otherwise to make products accessible to consumers easily
• Using Simulated Experience to Make Sense of Big Data also makes use of e-commerce to increase penetration and sales

11.3. Price

• The Using Simulated Experience to Make Sense of Big Data prices its products so that its target consumers can afford it easily
• Using Simulated Experience to Make Sense of Big Data uses relative pricing strategy for its products
• The price of Using Simulated Experience to Make Sense of Big Data’s products include not only the high quality raw materials and value additions but also the enhanced customer experience they deliver
• The company’s pricing strategy allows it to enjoy stable revenue and profit growth

11.4. Promotion

• The Using Simulated Experience to Make Sense of Big Data has a high budget allocated towards marketing activities
• The Using Simulated Experience to Make Sense of Big Data invests substantially in digital marketing activities to reap high and effective results
• Use of digital marketing has also allowed Using Simulated Experience to Make Sense of Big Data marketing strategy to cap costs and expenses
• Using Simulated Experience to Make Sense of Big Data also takes part in direct consumer engagement through on-ground activities where the company initiates trials
• Using Simulated Experience to Make Sense of Big Data also invests in traditional media channels to reach maximum consumers in the market

11.5. People

• Using Simulated Experience to Make Sense of Big Data has a large workforce across different companies
• This workforce is continually trained to become experts in their respective fields of operations
• Using Simulated Experience to Make Sense of Big Data hires without discrimination
• Using Simulated Experience to Make Sense of Big Data ensures that its employees remain motivated through building an inspirational and creative organizational culture
• Using Simulated Experience to Make Sense of Big Data focuses on also building and maintaining organizational commitment and loyalty in its employees

11.6. Process

• All activities at Using Simulated Experience to Make Sense of Big Data - from raw material procurement to the final sale to the end consumer - undergo systematic processes
• The processes at Using Simulated Experience to Make Sense of Big Data are well defined, and well communicated to all employees
• All employees are trained to follow the processes internally to ensure consistently high quality as well as timely production and deliveries
• The systematic processes also ensure a smooth running of operations at the Using Simulated Experience to Make Sense of Big Data

11.7. Physical evidence

• The physical evidence for Using Simulated Experience to Make Sense of Big Data includes the company logo, company store designs, and the product packaging
• Satisfied and excited customers in the retail spaces of Using Simulated Experience to Make Sense of Big Data, as well as during product consumption create a bubbling and an inviting atmosphere
• The e-commerce website for retail by Using Simulated Experience to Make Sense of Big Data is also designed with a friendly customer interface to allow maximum interaction with the brand
• The store designs created by Using Simulated Experience to Make Sense of Big Data for its retail space allow consumers maximum interaction with the products directly.

12. Promotional tactics for the marketing strategy of Using Simulated Experience to Make Sense of Big Data

12.1. Digital marketing

• The company uses social media for reaching consumers effectively
• The Using Simulated Experience to Make Sense of Big Data interacts with the consumers directly, and engages with them, answers their queries and takes their feedback
• The company also shares information and build relationships with consumers through digital marketing
• Using Simulated Experience to Make Sense of Big Data also makes use of blogging, emails, and content creations as a means of digital marketing

12.2. Conventional marketing

• The company uses a 360-degree approach in its marketing strategy
• This means that the company makes use of traditional marketing channels as well – such as TV, magazine adverts, and out of house placements

12.3. Influencers

• For direct, on-ground engagement, the company uses influencers
• Influencers interact with consumers directly, or through their channels of communication as a means of content creation and endorsing the Using Simulated Experience to Make Sense of Big Data brand

13. Monitoring and evaluation of the marketing strategy of Using Simulated Experience to Make Sense of Big Data

13.1. Changes in sales

• Using Simulated Experience to Make Sense of Big Data regularly tracks its sales to identify the effectiveness of its marketing strategy
• Increase in sales reflect the success of marketing strategy of Using Simulated Experience to Make Sense of Big Data
• Sometimes, Using Simulated Experience to Make Sense of Big Data experiences increase ins ae after some time of the launch of the marketing promotions

13.2. Surveys and focus groups

• Using Simulated Experience to Make Sense of Big Data frequently conducts focus groups and surveys to identify its brand worth
• These methods also help the company identify brand value, brand recall, and brand recognition
• Focus groups allow Using Simulated Experience to Make Sense of Big Data to gather feedback on its marketing strategy and helps it understand consumers better

13.3. ROI

• Effectiveness of marketing strategy of Using Simulated Experience to Make Sense of Big Data can also be seen through the revenue and profit growth
• Return on investment allows Using Simulated Experience to Make Sense of Big Data to effective gauge the effect and influence of the marketing strategy, and measure its success

13.4. Attainment of marketing objectives

• All marketing objectives set by Using Simulated Experience to Make Sense of Big Data are SMART
• The quantitative set against each of the marketing objective can facilitate attainment evaluation for the overall marketing strategy
• Successful and timely attainment of these marketing objectives highlight the success of the marketing strategy of Using Simulated Experience to Make Sense of Big Data