sonic selection case study

Sonic: Analyzing Customer Behavior Change During Covid

Introduction.

Sonic, the American drive-in fast-food chain, is one of the few businesses that have thrived during the restrictions of the pandemic. The restaurant’s unique format lends itself to success when dine-in is less popular, jumping from the 22nd most popular chain to the 5th in a matter of weeks according to foot traffic metrics captured by Ubermedia .

Sonic’s unique format lends itself to success when dine-in is less popular. How can Sonic understand who their new customers are and retain that audience as the economy moves back to a new normal?

To understand that, we must understand:

Who their customers are.
Why they came.
How to retain them.

Step 1: Identify Sonic's Unique Customer Base Before COVID

With PersonaLive, we can segment store visitors captured from the mobile movement data to build a ‘Sonic Customer Profile’. This profile provides insights into the types of people visiting Sonic locations prior to stay-at-home orders. In other words, this is the restaurant’s original customer base.

By matching the Sonic visitation data with Geosocial segments, we immediately see ranked patterns emerge:

Urban & Rural Mix: #JohnDeereCountry and #Southern Cross are rural segments while #UrbanDetermined and #UrbanFashion are more urban.
Fast Food Affinity: #JohnDeereCountry, #FlourishingFusion, and #OldTownRoad have a strong affinity for fast food restaurants like Whataburger, Chick-Fil-A, and Sonic.
Families: #FlourishingFusion’s prominence indicates people are coming from neighborhoods where family plays a prominent role.

Now that we know Sonic’s original customer profile, how has their customer base changed since stay-at-home orders were given? Are they getting more of the same customer or a totally new audience?

Step 2: Quantify the Change In Customer Base After COVID

Sonic’s loyal customer base.

We don’t see a heavy indication of change in the top five segments. The people coming to Sonic before COVID continue to go to Sonic afterwards.

‍ Therefore, to the extent that Sonic is already focusing on their core segments in site selection, local marketing, and broad advertising, they should continue to nurture these segments in the same way.

A New Emerging Audience

When we look at the customer segments with the greatest change, a clear story emerges; one that is nearly diametrically opposite of their Georgia core customer:

Family segments gained share of visits. #FlourishingFusion’s share of visitation increase, #Family&Faith broke into the top 10.
Rural consumers drove further. #Kids&Country and #DirtRoadDynasty had significant growth in visitation.
Breaking fast food norms. Higher income segments, which typically opt for healthier food options, are choosing to eat at Sonic more.

Step 3: Design Strategies To Retain the New Audience

So, how can Sonic capture and retain their newly found audience? We’ll now discuss possible strategies informed by these new insights.

Target emerging areas: The geography of visits changed. Focus advertising on areas contributing the most to store and those emerging.
Messaging and personalization: Create unique copy and ad sets to select segments. Change store design and menu to visitor types.
Deploy segment ads across channels: Export segment audiences across Facebook, Instagram, Google, Twitter, Snapchat, etc. Geotarget and personalize.

Sonic has an opportunity to benefit from changing consumer behavior. Sonic’s meteoric rise in popularity during COVID is a great test case for how a brand can stand to benefit from rapidly changing consumer behavior. Using PersonaLive, it is possible to capture the nuanced changes in customer profile and magnify that opportunity by designing strategies around customer interests.

Sonic: Analyzing Customer Behavior Change During Covid

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Case Study: SONIC Corp.

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Restaurant Chain Orders New Chief Marketing Officer: Pearson Partners Delivers

“We’re very happy with how well SONIC continues to grow as a brand,” says Anita Vanderveer, SONIC’s Vice President of People. “With growth, comes job opportunities and we’re excited about our constant development. One of several positions targeted was a senior vice president and chief marketing officer: a role that called for deep marketing experience in quick-service restaurant businesses serving meals around the clock. After eliminating other search firms through a request-for-proposal process, SONIC tapped Pearson Partners International to establish a profile of the ideal candidate and find the perfect person for the job.

“After using Pearson Partners for a series of executive searches, I admired their capability and credibility. “

Because shareholders were eager to see the CMO position occupied, filling the job quickly was just as important as finding the ideal candidate. Pearson Partners International worked around the clock with SONIC’s executive team, starting with a clear definition of the skills needed to take SONIC’s marketing to the next level.

In addition to the necessary experience in the quick-service restaurant business, “We needed someone who had already been an accomplished CMO and had a success story around what he or she had done and its impact on sales, traffic and dayparts,” Vanderveer says. “We were also looking for someone experienced in working with agencies and franchisees, as well as field marketing, consumer insights, product development, branding and strategy.”

“SONIC is what we consider the model client,” says Pearson. “They are in significant growth mode, and they see the value that Pearson Partners can bring in so many areas. Because we’ve done many searches for them, we really understand their culture, their operations, and what makes them unique and a great opportunity for candidates. We’re able to go out and get the right person with the right fit quickly.”

Working from the defined profile, Pearson Partners prescreened and presented over 16 candidates. In a series of meetings with Pearson Partners, the SONIC executive team – including Chairman of the Board and Chief Executive Officer Clifford Hudson – narrowed the field to six highly qualified individuals. After several rounds of interviews, assessments, Birkman testing and reference checks, two excellent candidates remained.

Just 51 days after taking on the search, Pearson Partners was pleased to announce that SONIC was ready to hire its new CMO. James O’Reilly, a 20-year consumer and restaurant marketing veteran, immediately accepted the job, relocating to Oklahoma City from his previous role as chief concept officer at a major quick-service restaurant company in Denver.

“James fit all of our criteria and in many ways, exceeded them,” Vanderveer says. “He had CMO experience from companies that had been successful, where he had made a positive impact on sales, traffic and dayparts, and he had that success story throughout his career. Not only was he someone who had done it and experienced it, he was really excited about SONIC and felt it was a dream position for him.”

Pearson Partners’ Birkman profiling also revealed that his personality and working style were a perfect fit with SONIC’s existing marketing team and senior leaders.

“The search was flawless,” Vanderveer says. “There was a lot of pressure, timing was of the essence, and we had to find the absolute best candidate. I am a huge fan of Pearson Partners. Both exceeded my expectations.”

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Purchase Order Automation

Sonic Manufacturing, headquartered in Silicon Valley’s Fremont, Calif., is widely known for having pushed the electronics industry toward the adoption of digital supply chain solutions. Back in 2016, “Orbweaver and Sonic had a good synergy,” explains David Ginsberg, vice president of Supply Chain, with both parties passionate about the feasibility of an integrated supply chain. Sonic, which was averaging a median supplier delivery of 30 days—from when they’d place an order to the time the parts were on the dock—was looking to shorten the lead time of their new product introduction (NPI) and production facility.

“One of the reasons people come to us is for speed. If you have to do everything manually, that limits your speed,” Ginsberg says. Sonic was looking for a solution that would receive their material requirements from their MRP, and convert those requirements to orders instantaneously. Stacks of printed paper and week-long lag times for orders just weren’t going to be sustainable in the future.

After connecting with Orbweaver, they reduced their 30-day delivery to a median of three days.

The velocity demonstrated by Sonic’s digitally integrated supply chain, “Supply Chains at the Speed of Light TM” , facilitated by Orbweaver’s solutions, has established a new standard for the industry, and has provided Sonic clear and sustainable market differentiation.

“Orbweaver has established trust and knows the right people to talk to at various companies. All the way around, from the technical implementation, to networking, to overcoming roadblocks, they’ve been great to work with.” David Ginsberg , Vice President of Supply Chain Sonic

Early API adoption

While it hadn’t been done before in the electronics industry, Sonic felt strongly about using APIs to automate its sourcing activity and the delivery of purchasing orders. Five years ago, electronic data interchange (EDI) solutions were the only tools available, and seemed misaligned with the nature of parts ordering. Designed for a snapshot in time (asynchronous), EDI would not provide the real-time accuracy and relevance of parts information that Sonic required. EDI communication alone would also create dependencies on external solutions that would be in conflict with the immediacy required by Sonic’s desired solution.

According to Sonic, their major distributors report that EDI accuracy is about 70%, meaning that 30% of orders marked for automatic processing are rejected, requiring “hand holding” resulting in a fundamentally expensive and inaccurate process. With API architecture, on the other hand, and the “touchless” API solutions provided by Orbweaver, Sonic is reporting about 98% of all automated orders are processed successfully. Just 2% require exception management.

Automation is Agile

Why API results in higher efficiency than the alternatives. There are three key reasons:

The biggest problem with EDI is that there is no validation of price, quantity, delivery and attributes at the time of order. Orders are placed against the customer historical record rather than against the supplier live record. It is “assumed” the supplier will ship as requested; and then degenerates into phone calls and emails when the supplier can’t. With real-time API integration, on the other hand, the availability is always accurate.
There’s a discrepancy in price accuracy, potentially between the real price and the price published on the Web site. Because Orbweaver offers peer-to-peer data connectivity, they are able to deliver client-specific, or contract based pricing: the price you pay, not the price published on the Web site. This data specificity allows systems to be automated while maintaining the unique relationship between buyers and sellers of components.
No match could be made with respect to availability, price, and/or lead time with the preferred trading partner. Orbweaver provides automated exception management related to the sourcing of components. With API, you query all the data from the supplier and simply convert their data to a purchase order. By definition, their own data must be accurate to them.

Results Realized

By using Orbweaver to enable system data flow, Sonic successfully automated its supplier selection and purchase order (PO) issuance processes. From MRP requirements analysis, to sourcing data, PO issuance, PO document delivery, PO document receipt confirmation, shipment, invoicing, all the way to parts delivery at the loading dock three days later, not a single Sonic employee touched a transaction and not a single piece of paper was printed.

Their prior method—subject to errors and delays—entailed printing out hundreds or thousands of MRP line items and passing them around weekly to their purchasing team. By electronically placing the matching POs using API integration, a majority of the ordering process can be done digitally in a matter of minutes.

By removing the intermediaries in favor of automation, almost instantly Sonic achieved a 40% placement rate, then climbing to 50%, and now, 65% automation straight to their dock. Automation is bringing in more than 50 million parts a year for Sonic. The numbers continue to climb as Orbweaver’s sourcing intelligence improves, as more distributors are added to Sonic’s network, and as distributors are able to offer increasingly rich data.

“We let the computers do repetition because they’re great at it and we let the people do problem solving because they’re great at it,” Ginsberg says, adding that Sonic’s buyers enjoy the more compelling responsibilities tied to sourcing the parts the system was unable to match!consisting mostly of obsolete, long lead time, and custom components that often go unidentified for weeks with legacy processes, now discoverable in minutes.

Cost Justification

Sonic, a small to medium-sized business, certainly doesn’t have the budget of a Fortune 500 company. But to achieve automation with Orbweaver, they didn’t need to. “The cost justification for this type of model is self-evident,” Ginsberg explains, noting that while every company approaches purchasing a bit differently, the efficiency achievements are significant regardless.

The average cost of a PO line item runs typically between $25- $100 in the industry, and occasionally up to $200 a line item. At Sonic’s current level of efficiency, they’re experiencing a cost of $0.25 per automated line item, a 100x to 400x improvement. And the benefit isn’t just with automated lines: the cost of a PO line item placed manually has also plummeted to just $5. In fact, just six Sonic employees on the purchasing team now manage the supply chain of 100 OEMs.

“From a business model standpoint, there’s simply no reason not to do this,” Ginsberg says.

This strategy has led to unprecedented efficiencies for Sonic, which orders one million parts for their factory every week. Half of these, 500,000, arrive three days after the order is placed. Annually, Sonic handles about 30,000 parts numbers, 1,000 bills of material, and about 10,000 brand new part number creations, moving in near real time.

“You can’t accomplish these kinds of business goals without automation. And you simply can’t find other solutions with this kind of payback,” says Ginsberg. “The benefits to the business are not just financial, but are evident in total performance and customer satisfaction as well.”

Sonic has also started using advanced ship notifications (ASNs) and electronic invoices available through Orbweaver, crossing the 50% mark on these to improve its overall communications.

A “Touchless” Future

More and more distributors are joining Sonic’s integration efforts. In recent weeks, two of their existing distributors pushed API updates and enhancements, while three more inquired about the technology. Remote working conditions due to the COVID-19 pandemic are likely contributing to the industry’s expanding drive toward automation. Orbweaver, which has a wide and deep existing integration with the electronics distribution industry, can help guide the transition.

Together, let’s make the electronics industry more agile, more efficient, and more relevant today. And let’s sell more parts.

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SONIC® drive-in cruises to success with Basware

Food & Beverage

North America

AP Automation

sonic-americas-drive-in-basware-customer

why basware
sonic america s drive in

When SONIC®, America’s Drive-In® wanted to make its accounts payable (AP) department more efficient, they turned to Basware’s invoice automation solution.

SONIC® is the largest chain of drive-in restaurants in the US. With over 3,500 company-owned and franchise locations from coast to coast, it’s known for its quick and personal Carhop service and made-to-order fast food.

In its accounts payable (AP) department, however, SONIC’s manual invoice processing operations were less efficient than desired, and it identified a need for improvement.

To address this need, SONIC set its sights on achieving the same degree of excellence and efficiency in AP as it had accomplished in its restaurant operations.

Key takeaways

E-invoice processing

Per year cost savings

Reduction in invoice staffing

SONIC's Challenges

The company’s previous manual AP processes were arduous and time-consuming. Each of the 458 company-owned restaurants across the US individually received invoices from suppliers. Twice monthly, these restaurants sent a batch of invoices to headquarters in Oklahoma City via UPS (United Parcel Service). The AP department had to key in each invoice manually.

Our process wasn’t working efficiently, we frequently didn’t receive invoices from the field promptly, and some were missing or lost. We also handled several calls from suppliers checking on the status of late or out-of-period invoices. Karen Higdon, Director of Accounts Payable, SONIC

Adding to these problems was the difficult task of manually handling 450,000 invoices each year. Once an invoice was entered, it was forwarded to another person for review for accuracy, making the invoice handling process even longer.

SONIC conducted a comprehensive technology selection process, sending RFIs to 15 companies before narrowing the field to three for more extensive review.

How SONIC implemented Basware's out of the box AP Automation solutions

SONIC chose Basware AP Automation for its outof-the-box functionality and set several goals for its automated system. The company wanted to reduce the time and effort involved in processing invoices, gain efficiencies, improve control over invoices and decrease late payments.

The implementation process went very smoothly. SONIC is “very pleased” with Basware and has created a true partnership. Higdon notes that “The Basware team has communicated frequently, effectively and is always available to come on-site on an as-needed basis.”

One of the biggest selling points of Basware AP Automation was that it offered robust functionality right out of the box. The fact that we were able to hit the ground running with the breadth and depth of functionality that we needed was important to us. Karen Higdon, Director of Accounts Payable, SONIC

The implemented solution supports electronic workflow, revision, cost allocation and approval of invoices, and transfer to the organization’s Infinium ERP.

Once invoices are in the system, Basware AP Automation’s autoflow capabilities route them to the appropriate approvers and reviewers in the company. To ensure that invoices are processed promptly, the Basware system sends out reminders and escalates approval to the next level if someone does not respond within a specific timeframe.

The system accommodates SONIC’s business rules by setting dollar limits on corporate approvals and establishing the approval chain for invoices. It also integrates seamlessly with SONIC’s Infinium ERP.

The solution provides complete access and display for all invoice information and spend analysis with its versatile reporting. SONIC also implemented Basware scan & capture to automate the conversion of paper invoices to electronic.

We are very pleased with the visibility and control achieved in Accounts Payable. We are particularly impressed with the cost savings and efficiencies we’ve been able to achieve, as well as our ability to pay invoices promptly and close our books faster and more accurately.

Karen Higdon, Director of Accounts Payable, SONIC

SONIC has realized important benefits using Basware AP Automation. The organization has saved substantial time, money, resource and reduced the volume of late payments it makes.

Digitization of AP processes significantly increased, with approximately 97% of the invoices now processed electronically. The integration between Basware SONIC’s Infinium ERP has also been seamless.

Transferring information between Basware and our ERP is a smooth process. We transfer information to our payables ledger several times a day and also send data to the general ledger once a week and it all happens very easily Ronda Boles, Supervisor of Payables at SONIC

By using invoices automation, SONIC has also reduced the need for storage. For seven years, the company previously-stored all invoices in paper format at an off-site facility. The annual cost for this was $160,000, making the removal of this activity a significant cost saving.

Overall, SONIC has reduced AP resources dedicated to invoices processing by more than 25%, decreasing the staff needed to work on this task from 19 to 13. At the same time, restaurant management has more time to focus on the core business, and supplier satisfaction has increased.

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Sonic Branding Success Story: Doritos

That's a mouthwatering CRUNCH.

higher recall than the CPG norm

higher appeal overall

The Objective

There are not many brands that can claim something as general as a triangle as their own, but somehow Frito-Lay has managed to do so with the Doritos brand. As consumers turned an increasing amount of attention to the audio channel, Frito-Lay focused less on the look of the Doritos brand, and more on its sound.

With taste, smell, a visual, and a solid cultural identity in the bag (so to speak), Frito-Lay set out to translate its unique and ‘disruptive’ brand identity across the airwaves.

The Approach

To tackle the challenge of building out a sonic identity as powerful and memorable as Dorito’s visual identity, the Frito-Lay team employed the expertise of sonic branding experts at Made Music Studio.

Made Music Studio developed cutting edge creatives to take listeners into an alternative Doritos-inspired world, without the visual aids.

Target audience: 18-24 year olds
Suite of assets from logos to longer sonic branding elements
Key brand attribute: “disruptive

Our Results

Frito-Lay released the suite of sounds within their ads. Not only did their new logo score 23% above the CPG norm for recall/memorability, the brand score showed 12% higher overall appeal for the brand after people heard the logo. Another important metric for the brand to get across in their sonic identity was creating disruption. Veritonic data showed that among their target audience of 18-24 year olds, 70% found the sound disruptive.

Download a copy of this case study.

The Doritos brand is known for being at the center of culture, and Frito-Lay’s stake in audio has proven the medium’s worth is here to stay. Hear the Doritos sonic identity in action in a 2- minute video released by Frito-Lay.

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Using support vector regression to estimate sonic log distributions: A case study from the Anadarko Basin, Oklahoma

2013, Journal of Petroleum Science and Engineering

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Iranian Journal of Oil and Gas Science and Technology

sadegh saffarzadeh

Porosity is one of the fundamental petrophysical properties that should be evaluated for hydrocarbon bearing reservoirs. It is a vital factor in precise understanding of reservoir quality in a hydrocarbon field. Log data are exceedingly crucial information in petroleum industries, for many of hydrocarbon parameters are obtained by virtue of petrophysical data. There are three main petrophysical logging tools for the determination of porosity, namely neutron, density, and sonic well logs. Porosity can be determined by the use of each of these tools; however, a precise analysis requires a complete set of these tools. Log sets are commonly either incomplete or unreliable for many reasons (i.e. incomplete logging, measurement errors, and loss of data owing to unsuitable data storage). To overcome this drawback, in this study several intelligent systems such as fuzzy logic (FL), neural network (NN), and support vector machine are used to predict synthesized petrophysical logs including n...

SEG AAPG Journal of Interpretation

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Planning and optimizing completion design for hydraulic fracturing require a quantifiable understanding of the spatial distribution of the brittleness of the rock and other geomechanical properties. Eventually, the goal is to maximize the stimulated reservoir volume with minimal cost overhead. The compressional and shear velocities (V P and V S , respectively) can also be used to calculate Young's modulus, Poisson's ratio, and other mechanical properties. In the field, sonic logs are not commonly acquired and operators often resort to regression to predict synthetic sonic logs. We have compared several machine learning regression techniques for their predictive ability to generate synthetic sonic (V P and V S) and a brittleness indicator, namely hardness, using the laboratory core data. We used techniques such as multilinear regression (MLR), least absolute shrinkage and selection operator regression, support vector regression, random forest (RF), gradient boosting (GB), and alternating conditional expectation. We found that the commonly used MLR is suboptimal with less-than-satisfactory predictive accuracies. Other techniques, particularly RF and GB, have greater predictive capabilities. We also used Gaussian process simulation for uncertainty quantification because it provides uncertainty estimates on the predicted values for a wide range of inputs. Random forest and extreme GB techniques also show low uncertainties in prediction.

Sarker Asish

Due to complexities in geologic structure, heterogeneity, and insufficient borehole information, shale formation faces challenges in accurately estimating the elastic properties of rock which triggers severe technical challenges in safe drilling and completion. These geomechanical properties could be computed from acoustic logs, however, accurate estimation is critical due to log deficit and a higher recovery expense of inadequate datasets. To fill the gap, this study focuses on predicting the sonic properties of rock using deep neural network (Bi-directional long short-time memory, Bi-LSTM) and random forest (RF) algorithms to estimate and evaluate the geomechanical properties of the potential unconventional formation, Permian Basin, situated in West Texas. A total of three wells were examined using both single-well and cross-well prediction algorithms. Log-derived single-well prediction models include a 75:25 ratio for training and testing the data whereas the cross-well includes ...

Saumen Maiti

Pore pressure (PP) study can provide insightful information about evolution history and/or geological process taking place over a region. Conventional methods, mostly are of deterministic, and they do not allow considering underlying variability and uncertainty. Here we implement Bayesian neural networks (BNN) optimized by Scaled Conjugate Gradient (SCG) and Hybrid Monte Carlo (HMC) approach to model the PP and estimate the uncertainty in prediction from well log data of well U1343E located at Bering sea slope region of the IODP Expedition 323. In the first step, to create representative samples of well log and corresponding PP samples, Eaton's and porosity methods are employed to estimate PP empirically from well log data (e.g., gamma ray, sonic velocity, bulk density and sonic derived porosity). In the second step, in total 357 representative samples are used to build a statistical model in Bayesian framework to model the PP against depth. Prior to actual data analysis, we conducted a series of experiments combining with auto-correlation function (ACF) and/or partial autocorrelation function (PACF) analysis to fix network structure (e.g., input lag, number of hidden node) and the bounds of network hyper-parameter. In contrast to previous approach, we seek to develop a mechanism which allows to explore the link between past PP and/or well log history and present PP under rapid sedimentation rate and changing environment. The model exhibits excellent performance between predicted and computed PP with Pearson's correlation coefficient, reduction of error (RE) (RE SCG-BNN $ 0.99; RE HMC-BNN $ 0.99), and index of agreement (IA) (IA SCG-BNN $ 0.99; IA HMC-BNN $ 0.98). Comparison based on coefficient of determination, R 2 , it is obtained that BNN produced superior results than the conventional artificial neural networks (ANNs). Moreover, at 530 mbsf (meter below sea floor), abrupt PP change could be linked to the transition from Pliocene to Pleistocene. The approach used here, could be useful to identify overpressure zones (OPZ) and to understand the role of past PP/well log to present PP history in many other complex geo-environmental applications.

Energy Reports

Amir Mosavi

Determination of pore pressure (PP), a key reservoir parameter that is beneficial for evaluating geomechanical parameters of the reservoir, is so important in oil and gas fields development. Accurate estimation of PP is also essential for safe drilling of oil and gas wells since PP data are used as the input for safe mud window determination. In the present study, empirical equations along with machine learning methods, namely random forest algorithm, support vector regression (SVR) algorithm, artificial neural network (ANN) algorithm, and decision tree (DT) algorithm, are employed for PP prediction applying well log data. To this end, 2827 data records collected from three wells (Well A, Well B, and Well C) drilled in one of the Middle East oil fields are used. The dataset of Wells A and B is used for models’ training, validating, and testing, while Well C dataset is applied for evaluating the models’ generalizability in PP prediction in the field under study. To construct the predictive algorithms, 12 input variables are initially considered in the study. A feature selection analysis is conducted to find the most influential input variables set for developing PP predictive models. The results obtained suggest that the 9-input-variable set is the most efficient combination of inputs used in the ML models construction. Among all the four ML algorithms proposed, the DT algorithm presents the most accurate predictions for PP, delivering R2 and RMSE values of 0.9985 and 14.460 psi, respectively. Furthermore, the model generalization analysis results reveal that the 9-input-variable DT model developed can be used for PP prediction throughout the field of study since it presented an excellent accuracy performance in predicting PP when applied to Well C dataset.

Journal of Natural Gas Science and Engineering

Sadegh Baziar

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